Search for periodic signals in the dielectron and diphoton invariant mass spectra using 139 fb⁻¹ of pp collisions at √s = 13 TeV with the ATLAS detector

JHEP10(2023)079 Published for SISSA by Springer Received:May 19, 2023 Revised:August 23, 2023 Accepted:September 29, 2023 Published:October 13, 2023 Search for periodic signals in the dielectron and diphoton invariant mass spectra using 139 fb−1of pp collisions at √s= 13 TeV with the ATLAS detector The ATLAS collaboration E-mail: [email protected] Abstract: A search for physics beyond the Standard Model inducing periodic signals in the dielectron and diphoton invariant mass spectra is presented using 139fb−1of √s= 13 TeV pp collision data collected by the ATLAS experiment at the LHC. Novel search techniques based on continuous wavelet transforms are used to infer the frequency of periodic signals from the invariant mass spectra and neural network classifiers are used to enhance the sensitivity to periodic resonances. In the absence of a signal, exclusion limits are placed at the 95% confidence level in the two-dimensional parameter space of the clockwork gravity model. Model-independent searches for deviations from the background-only hypothesis are also performed. Keywords: Beyond Standard Model, Hadron-Hadron Scattering, Particle and Resonance Production ArXiv ePrint: 2305.10894 Open Access, Copyright CERN, for the benefit of the ATLAS Collaboration. Article funded by SCOAP3. https://doi.org/10.1007/JHEP10(2023)079 JHEP10(2023)079 Contents 1 Introduction 1 2 Analysis strategy 2 3 ATLAS detector 4 4 Data and simulated event samples 4 5 Object and event selection 7 6 Signal modelling 8 7 Background modelling 9 8 Uncertainties 11 9 Generation of pseudo-experiments 13 10 Continuous wavelet transforms 14 11 Data analysis 15 11.1 Classifier NN 18 11.2 Autoencoder NN 19 12 Results 21 13 Conclusion 25 The ATLAS collaboration 31 1 Introduction Typical experimental searches for physics beyond the Standard Model (SM) at the Large Hadron Collider (LHC) search for new resonant peaks or non-resonant deviations in kinematic distributions, such as the invariant mass of two-particle systems. However, more complicated signatures are possible and have received little attention in experimental searches so far. One such possibility, which appears in several theoretical extensions to the SM, is a signal that gives rise to periodic structures. This type of signal is predicted by models with small mass splittings among many resonances. Extensions to the SM with these features include the continuum clockwork gravity model [1,2], which has an identical five-dimensional spacetime metric to the linear dilaton – 1 – JHEP10(2023)079 scenario [3,4]. This spacetime metric also approximates the dual of Little String Theory [5,6]. As the same theory can be interpreted as either a linear dilaton model or as the continuum version of a clockwork model, this theory is referred to as the Clockwork/Linear Dilaton (CW/LD) model. These models are well motivated, as the continuum spacetime version of the clockwork gravity model can address the Higgs boson mass naturalness problem [1]. The CW/LD model is also related to previous proposed solutions to the hierarchy problem, including theories with large extra dimensions [7,8] and Randall-Sundrum (RS) models where the extra dimension is warped [9]. The CW/LD model predicts a narrowly-spaced spectrum of resonances in mass. The mass spectrum and couplings of this tower of Kaluza-Klein (KK) gravitons are described by two parameters, kand M5. The parameter kis a mass parameter that determines the onset of the KK graviton spectrum and M5is the five-dimensional (5D) reduced Planck mass, the fundamental scale of the theory. As the cross-section for the CW/LD model approximately scales inversely with M5,σ∝1/M3 5, a limit on the signal strength in this search can be directly translated to a limit on M5for a given value of k. The mass splittings predicted by the CW/LD model are generally on the order of a few percent near the onset of the graviton spectrum and eventually decrease, falling below 1% for high graviton mode numbers [2]. Therefore, when looking at the invariant mass of the decay products of these resonances in experiments, the narrowly-spaced spectrum of resonances predicted by the model may appear as a long-range semi-periodic structure. The narrow spacing of the mass spectrum requires an excellent detector resolution in order to resolve these resonances which can be satisfied by relying on two electrons or two photons in the final state. This paper presents a search for periodic contributions to the diphoton and dielectron invariant mass spectra using 139 fb−1of √s= 13 TeV proton-proton (pp) collision data recorded by the ATLAS detector from 2015 to 2018 at the LHC. Previous searches for physics beyond the SM in high-mass diphoton final states and dielectron final states during Run 2 of the LHC using pp collisions at √s= 13 TeV were conducted by the ATLAS and CMS experiments [10–14]. The CMS experiment performed a search for the same CW/LD signal using only diphoton events in a search for a non-resonant excess at high invariant masses. The CMS result excludes values of M5in the range of 10TeV to 1TeV for values of kin the range of 0.1 GeV to 5 TeV [13]. 2 Analysis strategy This analysis is based on two previous ATLAS resonance searches performed in the dilepton [10] and diphoton [12] channels in the mass range of 225–6000 GeV and 150–5000 GeV, respectively. The same data samples and background estimation methods were used, in both cases, relying on data-driven techniques. The two previous analyses also estimated the background from simulation and found excellent compatibility with the estimations from data. Therefore, this analysis takes advantage of the precise data-driven background estimates while also benefitting from the availability of a simulation-based background estimates. The background estimates from simulation include a complete set of systematic uncertainties in both of the analysis channels, which is important for this analysis because – 2 – JHEP10(2023)079 the “spurious-signal” uncertainty derived in refs. [10,12] is applicable only to searches which consider a single narrow resonance. The simulation-based uncertainties from previous searches are used instead and their effect on the background estimation is incorporated using the methodology developed in a previous ATLAS search for non-resonant signatures in the dilepton channel [11]. Finally, this analysis also relies on transformational and statistical methods developed in ref. [15] in order to deal with data featuring periodic structures. A brief description of the ATLAS detector is given in section 3. The data and simulated event samples are discussed in section 4and an overview of the event selection in each analysis channel is described in section 5. The signal modelling is presented in section 6 and the background estimation methods are discussed briefly in section 7. The sources of systematic uncertainties are discussed in section 8, while the uncertainty estimation process is described in section 9. This analysis performs a search for generic periodic features in the dielectron and diphoton invariant mass spectra, in the same mass ranges considered in refs. [10,12]. The dielectron and diphoton channels are analysed separately due to potential overlaps in their event selections. This search uses a continuous wavelet transform (CWT) to analyse these mass spectra in the frequency domain.1The output of the CWT is a two-dimensional image of the wavelet amplitude in the frequency versus mass space, referred to as a “scalogram.” The potential periodicity of a signal can be revealed in the image, for example as a local “blob” around a small range of frequencies or masses. While conventional resonant and non-resonant searches may be suboptimal, particularly in the case of small CW/LD-like signals, the CWT may still provide an enhancement of a new signal’s semi-periodic nature. This enhancement provides a clear separation of a signal from the background and suggests that the search should be conducted in the CWT space rather than in the mass space alone. The CWT method is described briefly in section 10. In the limit of infinite statistical precision, the local features of a potential signal in the CWT images can be clearly visible and separable from the background-only case. When accounting for realistic statistical fluctuations due to the finite-size distributions in the mass space, the signal features wash out partially in the CWT images and the separation power becomes significantly smaller. Therefore, machine-learning techniques are applied to distinguish periodic contributions to the diphoton and dielectron CWT images made using the invariant mass spectra. Model-independent results are provided using an autoencoder-based anomaly detection procedure to search for generic periodic deviations in the scalograms. The CW/LD model is used as a benchmark model for a model-specific search using the CWT images. A neural network binary classifier is trained on the CWT images made from background-only and signal-plus background mass distributions to provide a test statistic for discovery of potential periodic resonances with specific kand M5 values. In both the model-independent and model-specific searches, the test-statistic used during the statistical inference portion of the analysis is based on the machine-learning outputs themselves. As the CW/LD signal may introduce a non-resonant enhancement above the SM background in the mass space, the statistical inference in the two searches can be 1In this analysis, the transformed variable is the invariant mass rather than time. – 3 – JHEP10(2023)079 dominated by the non-resonant signal features instead of the semi-periodic contribution. Therefore, the two searches are performed with and without specific thresholds imposed to reduce potential non-resonant contributions and focus the sensitivity on the periodic features. The statistical analysis of the two methods and the related thresholds are discussed in section 11. The model-independent results and the model-specific exclusion limits set in the k-M5plane of the CW/LD model are given in section 12. The previous dilepton analysis [10] also included a dimuon analysis channel that could be sensitive to a potential CW/LD signal. However, the mass resolution becomes much worse for high-mass dimuon events compared with the dielectron channel. These resolution effects cause the individual modes in the KK tower to merge and the signal periodicity cannot be resolved. For this reason, the dimuon channel is not used in this search. 3 ATLAS detector The ATLAS experiment [16–18] is a multipurpose particle detector with a forwardbackward symmetric cylindrical geometry and nearly 4πcoverage in solid angle.2It consists of an inner tracking detector surrounded by a thin superconducting solenoid providing a 2T axial magnetic field, electromagnetic and hadronic calorimeters, and a muon spectrometer. The inner detector (ID) covers the pseudorapidity range |η|<2.5and consists of silicon pixel, silicon microstrip, and transition radiation tracking detectors. Lead/liquidargon (LAr) sampling calorimeters provide electromagnetic (EM) energy measurements with high granularity. A steel/scintillating-tile hadron sampling calorimeter covers the central pseudorapidity range (|η|<1.7). The endcap and forward regions are instrumented with LAr calorimeters for both the EM and hadronic energy measurements up to |η|= 4.9. The muon spectrometer surrounds the calorimeters, covering the region |η|<2.7 and is based on three large superconducting air-core toroidal magnets with eight coils each. The field integral of the toroids ranges between 2.0 and 6.0 Tm across most of the detector acceptance. A two-level trigger system is used to select events. The first-level trigger is implemented in custom hardware and uses a subset of the detector information to accept events at a rate below 100 kHz. A software-based trigger then reduces the accepted event rate to an average of 1 kHz for offline storage [19]. An extensive software suite [20] is used in the reconstruction and analysis of real and simulated data, in detector operations, and in the trigger and data acquisition systems of the experiment. 4 Data and simulated event samples The data used for the search consists of the pp collision data recorded by the ATLAS experiment at √s= 13 TeV during the 2015 to 2018 LHC data-taking period. After requir2ATLAS uses a right-handed coordinate system with its origin at the nominal interaction point (IP) in the centre of the detector and the z-axis along the beam pipe. The x-axis points from the IP to the centre of the LHC ring, and the y-axis points upward. Cylindrical coordinates (r, φ)are used in the transverse plane, φbeing the azimuthal angle around the z-axis. The pseudorapidity is defined in terms of the polar angle θas η=−ln tan(θ/2). – 4 – JHEP10(2023)079 ing stable beam conditions and data quality selections with all ATLAS subsystems operational [21], the data sample corresponds to an integrated luminosity of 139.0 ±2.4 fb−1[22]. The LUCID-2 detector [23] was used for the primary luminosity measurements. The events used in this analysis were recorded using a set of diphoton and dielectron triggers. Events in the diphoton channel were recorded using a diphoton trigger that required at least two energy clusters in the EM calorimeter with transverse energies (ET) greater than 35 and 25GeV for the ET-ordered leading and subleading photon candidates, respectively. Both of the clusters were required to satisfy photon identification criteria based on the shower shapes in the EM calorimeters. The triggers used in 2015 and 2016 required both of the photons to satisfy the loose identification requirement [24]. In 2017 and 2018, due to the greater instantaneous luminosity, the diphoton trigger requirements were tightened and both of the photons were required to satisfy the medium identification requirement. The efficiency of the diphoton trigger relative to the event selection given in section 5is over 99% for the 2015–2016 data and above 98% for the 2017–2018 data [25]. Events in the dielectron channel were collected using several dielectron triggers. The trigger used in 2015 required both of the electrons to satisfy the loose identification criteria and ETthresholds of 12GeV. In 2016, the ETthresholds were increased to 17 GeV for both electrons and the electrons were required to satisfy the very loose identification criteria [24]. In 2017 and 2018, the identification criteria were left unchanged and the ETthresholds were increased to 24GeV for both of the electrons [25]. Although the background in this analysis is estimated by using data-driven methods, simulated Monte Carlo (MC) events are used to optimise the analysis selections, determine appropriate fit functions for the data, estimate background compositions, and evaluate signal acceptances and efficiencies. As the KK modes in the CW/LD model are on-shell, the interference effects between the resonant signals and all background processes are neglected in both the diphoton and dielectron channels. For the diphoton channel, the largest background comes from the production of two prompt photons which represents the irreducible background in this search channel. Smaller background contributions come from events containing a photon and a jet and events with two jets, where the jets are misidentified as photons. These smaller backgrounds are estimated by using a data-driven technique, the two-dimensional sideband method, described in ref. [26]. Events with two prompt photons were simulated using the Sherpa 2.2.4 [27,28] event generator. Matrix elements were calculated with up to one additional parton at next-toleading-order (NLO) and including two or three additional partons at leading-order (LO) in perturbative QCD (pQCD). These matrix elements were merged with the Sherpa partonshower simulation using the ME+PS@NLO prescription [29–32]. The NNPDF3.0nnlo parton distribution function (PDF) set [33] was used and paired with a dedicated partonshower tune in the Sherpa generator. For the dielectron channel, the main prompt backgrounds arise from Drell-Yan (DY), top-quark pair (t¯ t), single-top-quark, and diboson production. The background contribution from non-prompt electrons from multijet and W+ jets processes is estimated by using a data-driven technique, the matrix method, as described in ref. [34]. – 5 – JHEP10(2023)079 The DY [35] sample was generated using the Powheg Box v1 [36–39] event generator with the CT10 PDF set [40] and interfaced with the Pythia 8.186 [41] parton shower program. Next-to-next-to-leading-order (NNLO) corrections in pQCD and NLO corrections in electroweak (EW) theory were calculated and applied to the simulated DY events. The pQCD corrections were computed with VRAP v0.9 [42] and the CT14 NNLO PDF set [43]. The EW corrections were computed with MCSANC [44] which accounts for quantum electrodynamic effects due to initial-state radiation, interference between initial and final-state radiation, and Sudakov logarithm single-loop corrections. The diboson [45] processes with fully leptonic and semileptonic final states were simulated using Sherpa 2.2.1 with the CT10 PDF set. Matrix elements were calculated at NLO accuracy in QCD for up to one additional parton and at LO accuracy for up to three additional parton emissions. The matrix element calculations were matched and merged with the Sherpa parton shower based on Catani-Seymour dipole factorisation [46,47] using the ME+PS@NLO prescription. The diboson and DY backgrounds were generated in slices of dilepton mass in order to enhance the MC statistical precision in the high-mass region. The t¯ tand single-top-quark samples were generated with Powheg Box v2 [36–38,48– 51] at NLO using the NNPDF3.0nlo PDF [33] in the matrix element and interfaced to Pythia 8.230 [52] in order to model the parton shower, hadronisation, and underlying event, with parameters set according to the A14 tune [53] and using the NNPDF2.3lo set of PDFs [54]. These samples were normalised to the theoretical cross-sections calculated at NNLO in pQCD and include resummation of the next-to-next-to-leading logarithmic soft gluon terms as provided by Top++ 2.0 [55]. Resonant single-graviton MC samples were simulated using a Randall-Sundrum model [9]. These MC samples were generated at LO in pQCD using Pythia 8 with the NNPDF2.3lo PDF set and the A14 tune. In these samples, only the lightest KK graviton excitation was generated. For the diphoton decay channel, the samples were generated with a KK graviton mass mG∗in the range of 150–5000GeV. A fixed coupling value of k/MPl = 0.01, where MPl =MPl/√8πis the reduced Planck scale, was used to ensure a sufficiently narrow-width signal. For the dielectron decay channel, samples were generated with masses in the range of 200–6000GeV and with a fixed coupling value of k/MPl = 0.1. These samples are used to determine the acceptance and efficiency of selecting the CW/LD signal. Additional MC samples incorporating a series of RS graviton resonances were generated at LO in pQCD using Pythia 8.244 with the NNPDF2.3lo PDF set and the A14 tune. In these samples, only the direct graviton decays into dielectron and diphoton final-states were simulated. Samples were generated for kvalues of 300GeV, 500GeV, 1TeV, and 2TeV with M5fixed to a value of 6TeV. These samples are used to validate the analytic signal templates of the CW/LD signals, which are discussed in detail in section 6. The effects of multiple pp interactions in the same bunch crossing as that of the hard scatter plus those from adjacent bunch crossings (pile-up) are included in all simulated samples. Pile-up collisions were generated with Pythia 8.186 using the NNPDF2.3lo PDF set and the ATLAS A3 set of tuned parameters [56]. Simulated event samples were weighted to reproduce the distribution of the average number of interactions per bunch crossing observed in the data [57]. – 6 – JHEP10(2023)079 The spin-2 diphoton simulated signal events, and DY, t¯ t, single-top-quark, and diboson background events were processed using a detailed simulation of the ATLAS detector [58] based on Geant4 [59]. The irreducible prompt γγ background and spin-2 dielectron signals were processed using a fast simulation of the ATLAS detector [60], where the full simulation of the calorimeter is replaced with a fast parameterisation of the calorimeter response. All simulated events were reconstructed with the same reconstruction algorithms as those used for data. Generator-level-only MC samples of the NLO DY background are used for the modelling studies described in section 7. These samples could not be produced with the ATLAS detector simulation due to the large number of events required [10]. 5 Object and event selection Complete descriptions of the object definitions and event selections are given in ref. [10] for the dielectron channel and ref. [12] for the diphoton channel. The criteria are identical to the ones applied in this analysis and a brief description is given here. The event selection in the diphoton channel requires at least two reconstructed photon candidates with ET>25 GeV and |η|<2.37, excluding candidates in the transition region 1.37 <|η|<1.52 between the barrel and endcap EM calorimeters. The two highest-ET photons are used to form the diphoton candidate and are used with additional information from the tracking detectors to identify the diphoton primary vertex [61]. After the diphoton primary vertex is identified, the leading and subleading photons are required to have ET/mγγ >0.35 and 0.25, respectively. The diphoton invariant mass, mγγ, is required to be greater than 150GeV. To reduce the background from jets, photon candidates must satisfy the tight identification criteria based on shower shapes in the EM calorimeter [24]. Events in the dielectron channel are selected by requiring at least one pair of reconstructed electron candidates. Each event is required to contain at least one reconstructed pp interaction vertex, where the primary vertex is defined as the vertex with the highest sum of track transverse momenta squared. Electron candidates are reconstructed from ID tracks matched to energy clusters deposited in the EM calorimeter [24,62]. All selected electrons are required to have ET>30 GeV and |η|<2.47. As in the diphoton channel, electron candidates in the transition region (1.37 <|η|<1.52) are not considered. The final selection requires that the electrons satisfy the medium identification working point. Electron candidate tracks are required to satisfy |d0/σ(d0)|<5and |z0sinθ|<0.5mm, where d0and z0are the transverse and longitudinal impact parameters defined relative to the primary vertex position and σ(d0)is the uncertainty on d0. This selection ensures that the electron candidate track is consistent with the primary vertex of the event. If there are more than two electrons in the same event, the two electrons with the highest ETare selected to form the dielectron pair. An opposite-sign electron charge requirement is not applied due to a high probability of charge misidentification for highETelectrons. The reconstructed invariant mass of the dielectron pair, mee, is required to be above 225GeV to avoid the region dominated by decays of the Zboson which cannot be described using the same background parameterisation as the high-mass region. – 7 – JHEP10(2023)079 To suppress the backgrounds from misidentified jets, the photon and electron candidates are required to satisfy calorimeterand track-based isolation criteria. The photons are required to satisfy the tight isolation which is 90–95% efficient for the analysis selection [24]. Electrons are required to satisfy the gradient isolation which is 99% efficient for the analysis selection [24]. 6 Signal modelling The CW/LD signals are modelled using analytic invariant mass templates constructed from a range of inputs including PDF information, branching ratio, and detector resolution modelling derived from the previous ATLAS diphoton and dielectron analyses. For each reconstructed mass bin, the expected number of signal events is determined by computing the contribution from the entire KK graviton tower. The estimate of the expected number of signal events for a given reconstructed mass bin iis given by Nreco i=Lint ·X G σG·BG(G→XX)·P(Mi|MG)·(A×)G(6.1) where the index Gruns over the KK graviton modes whose masses are within the search range. In eq. (6.1), Lint is the integrated luminosity, σGis the production cross-section, BG denotes the branching ratio, and (A×)Gis the acceptance times efficiency for a given mass, MG, of the KK graviton. The P(Mi|MG)term represents the probability to reconstruct an event in invariant mass bin ifor events with an input true mass of MGthat satisfy the selection requirements and is referred to as the transfer function (TF). The TF provides a smooth transformation between the true and reconstructed invariant mass spectrum by modelling the detector effects. The convolution becomes a product as the true masses MG are discrete and the natural widths can be neglected as the resonances are described by the narrow-width-approximation (NWA) over the range of true masses considered in this analysis. The cross-sections and branching ratios for the gravitons in the CW/LD signal are taken from ref. [2], where the effects of heavy graviton cascade decays into lighter graviton pairs are included in the total width when calculating the diphoton and dielectron branching ratios. The additional contributions to the signal from these cascade decays into diphoton and dielectron final states are not considered. The TF and acceptance times efficiency terms are derived following the methodologies of the previous ATLAS dielectron and diphoton resonance searches [10,12] and are briefly outlined below. These terms are derived using the DY MC samples and the resonant single-graviton MC samples for the dielectron and diphoton channels, respectively. For the dielectron channel, the detector response is defined in the TF with respect to the relative dilepton mass resolution (m`` −mtrue `` )/mtrue `` , where mtrue `` is the generated dilepton mass at Born level before final-state radiation. The mass resolution is parameterised as the sum of a Gaussian distribution and a Crystal Ball function [63,64]. The Gaussian component in this function describes the central peak of the detector response and the Crystal Ball component is used to model the effects of bremsstrahlung in the dielectron – 8 – JHEP10(2023)079 Figure 3. An illustration of the complex Morlet Wavelet used in the continuous wavelet transform in this analysis, split into real and imaginary components. Example scalograms for the dielectron and diphoton channels can be seen in figures 4 (without statistical fluctuation) and 5(with statistical fluctuation). These scalograms are created after applying the CWT on a background-only distribution (figures 4a,4b,5a and 5b) and on a signal-plus-background distribution (figures 4c,4d,5c and 5d). The saturated area in the high-scale region occurs because the background distribution is not flat. Therefore, the background may still appear as periodic for very large scales, that is, those scales with small frequencies. The sharp transition seen between low |W(α, β)|and high |W(α, β)|values occurs because the |W(α, β)|values change quickly relative to the scale chosen for the z-axis. The signal contribution in figures 5c and 5d is clearly seen as a local small “island,” discernible from the continuum of the background shown in figures 5a and 5b, even when realistic statistical fluctuations are included. This signal-island represents the locality of the signal in both the mass and the scale (or frequency) spaces. Fixing M5and increasing the value of kshifts the signal-island in two ways. The signal-island is shifted horizontally to higher masses because the signal turn-on point in mass is roughly equal to the value of k. The signal-island is also shifted vertically towards higher scales because the spacing in the KK tower increases with k. Fixing kand changing M5only determines the prominence of the island, that is, it becomes more distinct with decreasing M5because of the inverse relationship between M5and the signal cross-section. Changing the mass resolution due to detector effects (within the uncertainties) only affects the |W(α, β)|values of the signalisland and this change is effectively equivalent to changing M5. 11 Data analysis The diphoton and dielectron invariant mass distributions from the toys detailed in section 9 are transformed into images using the CWT method described in section 10 before being used to train a neural network (NN). The diphoton and dielectron images are treated as independent channels and are trained separately. Two types of convolutional NNs are used in this search, a classifier NN and an autoencoder (AE), which are discussed in sections 11.1 and 11.2, respectively. In both of the cases, the training is done using stat-toys, while the prediction is performed using syst-toys to evaluate the impact of the systematic – 15 – JHEP10(2023)079 1000 2000 3000 4000 5000 6000 mee[GeV] 1 3 10 30 100 300 1000 [GeV] p s=13 TeV, 139 fb 1 ATLAS Simulation 0.00 0.25 0.50 0.75 1.00 1.25 1.50 1.75 2.00 |W( ,mee)| [GeV 1/2] (a) 1000 2000 3000 4000 5000 m [GeV] 1 3 10 30 100 300 1000 [GeV] p s=13 TeV, 139 fb 1 ATLAS Simulation 0.00 0.25 0.50 0.75 1.00 1.25 1.50 1.75 2.00 |W( ,m )| [GeV 1/2] (b) 1000 2000 3000 4000 5000 6000 mee[GeV] 1 3 10 30 100 300 1000 [GeV] p s=13 TeV, 139 fb 1 ATLAS Simulation 0.00 0.25 0.50 0.75 1.00 1.25 1.50 1.75 2.00 |W( ,mee)| [GeV 1/2] (c) 1000 2000 3000 4000 5000 m [GeV] 1 3 10 30 100 300 1000 [GeV] p s=13 TeV, 139 fb 1 ATLAS Simulation 0.00 0.25 0.50 0.75 1.00 1.25 1.50 1.75 2.00 |W( ,m )| [GeV 1/2] (d) Figure 4. Scalogram output of the CWT of (a) dielectron and (b) diphoton background-only toy experiments and (c) dielectron and (d) diphoton signal-plus-background toy experiments with k= 1200 GeV and M5= 3000 GeV. These toys are produced without Poisson fluctuations. The signal contribution in (c) and (d) manifests as a localised “island” in mass and scale, discernible from the continuum of the background shown in (a) and (b). The scalograms are provided here with a mass binning of 1GeV. Here αis the CWT scale parameter and W(α, β)are the wavelet coefficients defined in eq. (10.1), where the invariant mass of each channel takes the role of β. – 16 – JHEP10(2023)079 1000 2000 3000 4000 5000 6000 mee[GeV] 1 3 10 30 100 300 1000 [GeV] p s=13 TeV, 139 fb 1 ATLAS Simulation 0.00 0.25 0.50 0.75 1.00 1.25 1.50 1.75 2.00 |W( ,mee)| [GeV 1/2] (a) 1000 2000 3000 4000 5000 m [GeV] 1 3 10 30 100 300 1000 [GeV] p s=13 TeV, 139 fb 1 ATLAS Simulation 0.00 0.25 0.50 0.75 1.00 1.25 1.50 1.75 2.00 |W( ,m )| [GeV 1/2] (b) 1000 2000 3000 4000 5000 6000 mee[GeV] 1 3 10 30 100 300 1000 [GeV] p s=13 TeV, 139 fb 1 ATLAS Simulation 0.00 0.25 0.50 0.75 1.00 1.25 1.50 1.75 2.00 |W( ,mee)| [GeV 1/2] (c) 1000 2000 3000 4000 5000 m [GeV] 1 3 10 30 100 300 1000 [GeV] p s=13 TeV, 139 fb 1 ATLAS Simulation 0.00 0.25 0.50 0.75 1.00 1.25 1.50 1.75 2.00 |W( ,m )| [GeV 1/2] (d) Figure 5. Scalogram output of the CWT of (a) dielectron and (b) diphoton background-only toy experiments and (c) dielectron and (d) diphoton signal-plus-background toy experiments with k= 1200 GeV and M5= 3000 GeV. These toys are produced with Poisson fluctuations arising from the statistical uncertainty of the data sample. These fluctuations are shown to dilute the presence of a signal. The signal contribution in (c) and (d) manifests as a localised “island” in mass and scale, discernible from the continuum of the background shown in (a) and (b). The scalograms are provided here with a mass binning of 1 GeV. Here αis the CWT scale parameter and W(α, β)are the wavelet coefficients defined in eq. (10.1), where the invariant mass of each channel takes the role of β. – 17 – JHEP10(2023)079 uncertainties in the NN responses. As discussed in section 9, the syst-toys already include a proper statistical representation of the data. The classifier NN is used to probe for the periodic signals of the CW/LD model specifically (denoted here as model-dependent). On the other hand, the AE NN is used to search for more general anomalies in the data (denoted here as model-independent). Both of the neural networks used in this analysis are based on the setups given in ref. [15]. The neural networks are implemented in Keras [71] using the TensorFlow backend [72]. The ADAM [73] optimiser is used to minimise the loss functions in each NN setup. The search is performed using a general form of a test statistic derived from the NN output itself. The loss functions associated with each of the NNs play the role of the likelihood function in a typical analysis. The loss function used in the classifier NN is the Binary Cross-Entropy (BCE) [74], while the AE NN uses the Mean Squared Error (MSE) loss function. The MSE loss function is further discussed in section 11.2. The training and validation loss are recorded as a function of the number of epochs and are inspected to verify that each NN gives good agreement in the output of the loss function when comparing the training and validation datasets. In this analysis, the NN output in the classifier setup and the value of the loss function in the AE setup are used as test statistics. To set the exclusion limits on the modeldependent parameter space, the modified frequentist method, commonly known as the CLsmethod [75–77], is used. This choice results in more conservative limits than those obtained by the standard p-value procedure [75,76]. The dielectron and diphoton channels are analysed separately due to potential overlaps in their event selections. 11.1 Classifier NN One possible way to discover a specific signal in a scalogram is with a classifier NN. In this analysis, the convolutional NN classifier setup from ref. [15] is implemented for the modeldependent search. The CWT scalogram of the mass spectrum for each stat-toy, syst-toy, and the signal region data is produced and rebinned to ensure the inputs to the NN remain manageable. The mass for the NN inputs is rebinned into 40 GeV wide bins, while the scale is binned logarithmically. The stat-toy scalograms for the background-only and signal-plusbackground cases are used for training the binary classifier convolutional NN, where the output of the network is between 0(background-like) and 1(signal-plus-background like). In addition to the signal periodicity of the CW/LD model, these models can introduce a non-resonant tail above the SM background at high dielectron or diphoton invariant masses. For these high-mass ranges, the periodicity may no longer be as apparent due to the widening of the resonances with mass. This signal widening occurs because the experimental mass resolution worsens, which causes the merging of the signal mass peaks. This analysis focuses on the periodic features of the signal by adjusting the procedure such that the subsequent inference does not consider the effects of the non-resonant tail. To achieve this adjustment, a cutoff is applied to the dielectron or diphoton mass distribution to remove events above a certain point in mass prior to training the classifier. This mass threshold is determined by calculating a signal’s local significance, S/σB, where Sis the signal yield and σBis the local per-bin uncertainty. This uncertainty includes – 18 – JHEP10(2023)079 the statistical uncertainty and the systematic uncertainties described in section 8, added in quadrature, of the background. The bin in the invariant mass distribution on a local signal peak where the per-bin-significance falls below 50% of its maximal value is chosen as the threshold. While the cutoff is independent of M5by construction, it does rise monotonically with k. Therefore, the cutoff values are calculated for a few points in k and are then fit with a second-order polynomial to allow for interpolation between the k points. As the long-range signal shape falls rapidly after reaching its maximum, alternative choices close to the nominal choice of 50% of the maximum local significance have little impact on the result. A lower significance choice, for example S/σB≈10% of the maximal value, effectively introduces no cutoff and the subsequent inference remains affected by the presence of the non-resonant tail feature. A higher significance choice, e.g., S/σB≈90% of the maximum value, removes the non-resonant tail effectively but also removes a large mass range where the periodicity is still apparent and reduces the search sensitivity to the intended feature. For the dielectron channel, the 50% threshold mass value ranges from 1400 to 6000GeV for kin the range of 200GeV to 5850GeV. For the diphoton channel, the mass threshold ranges from 2000 to 5000GeV for kin the range of 150GeV to 4900 GeV. For completeness, the classifier search without mass thresholds is also performed. In this case, the non-resonant feature of the signal may be present, while the periodicity may be unresolvable in the high mass region. The CWT and NN procedure can still provide some discrimination power between the signal-plus-background and backgroundonly distributions due to the non-resonant feature and additional oscillations available to describe the signal. Therefore, the sensitivity for low-k(k.1000 GeV) signals is expected to be higher than for the case with mass thresholds. For high-kvalues, (k&1000 GeV), the sensitivities of the two methods are expected to be almost identical. 11.2 Autoencoder NN A model-independent approach searching for anomalies in scalograms is performed using AEs. This technique has previously been applied to jet images in refs. [78,79] and was implemented and suggested for this type of search in ref. [15]. The AE compresses a scalogram to a smaller set of parameters, which are then used to reconstruct the input scalogram. Instead of using the original background-only scalograms, they are standardised such that each bin in the scalogram is remapped to a local p-value in the range 0 to 1. The remapping per bin is done using the distribution of the original values in the specific bin from multiple scalograms. Further, to regularise the output, the negative logarithm of the remapped bin value (local p-value) is used to build a new two-dimensional input. This procedure is identical to the procedure in ref. [15] and ensures that the loss function is not dominated by the region with high statistical precision. The AE is trained on backgroundonly, stat-toy remapped scalograms to reproduce the original remapped scalograms (y) by minimising the MSE loss function [79], LMSE, as defined below: LMSE(y,ˆy) = 1 n n X i=1 |yi−ˆyi|2,(11.1) – 19 – JHEP10(2023)079 Threshold Dielectron Mass Range [GeV] Diphoton Mass Range [GeV] R < 10% 225–1520 150–1380 R < 50% 225–2700 150–2400 No threshold 225–4400 150–2700 Table 1. Different precision-driven thresholds considered for each analysis channel and the corresponding mass ranges probed in the model-independent analysis. For the no threshold scenario, the upper limit in mass is chosen such that the range goes 300 GeV beyond the last observed data point in the dielectron and diphoton mass spectra. where nis the number of bins used in the scalogram and ˆy is the output remapped scalogram from the AE. After the AE model is trained, the AE should be able to approximately reproduce the original remapped scalogram if applied to a typical background-only syst-toy scalogram. Conversely, the AE may fail to reproduce the scalogram if applied to a data scalogram that contains a signal. This metric, LMSE, is used as a test statistic. In this analysis, the AE is particularly sensitive to signals with periodic structures. The LMSE is also calculated per scalogram bin. For each bin, an ensemble of the LMSE values for the background predictions is generated. From these ensembles, a local p-value is calculated for every scalogram bin. The statistically significant bins are then visible with p-values close to 0. As discussed in section 11.1, to focus the search on the periodicity features and avoid possible inference that is based only on non-resonant signals, two sets of selections are introduced. Unlike in section 11.1, these selections cannot be based on a specific signal and should be characterised only based on information pertaining to the SM backgrounds. In the first method, the non-resonant features are removed by using the statistical error at the tail of the background template shape, R= 1/pB(mmin), where B(mmin)is the integral of the background template shape above some minimum invariant mass mmin. Different mmin thresholds are derived for different Rvalues, where for each, the tail of the distribution above the threshold is removed before calculating the CWT. The thresholds and the respective invariant mass ranges for each channel are found in table 1. This procedure also prevents large statistical fluctuations in the high-mass tail of the background distribution from affecting the LMSE of the AE predictions. In the second approach, the scale range of the scalograms is instead modified and the mass range remains unrestricted. This approach, referred to as scale thresholding, avoids the large-scale region of the scalogram that may include continuum contributions from a signal. In this method, the bin content in the scalogram is replaced with zero if the scale αin that bin satisfies α > m/4, where mdenotes the mass value of that bin. In a similar way, to exclude the scales that are short and cannot be resolved experimentally, the bin content in the scalogram is replaced with zero if the bin satisfies α < m/100. The results from both methods are given in section 12. Possible deviations in the data, arising from SM or beyond the SM effects, from the smoothly falling background model assumed in each analysis channel could be captured in – 20 – JHEP10(2023)079 the AE analysis. However, the sensitivity to any new signal is lessened as this approach is designed to be most sensitive for deviations which appear periodic. 12 Results The scalograms for the data events passing all selections for the diphoton and dielectron channels are shown in figure 6. The model-independent results from the AE-based search with R < 50% are presented as the negative logarithm of the local p-values corresponding to these data scalograms and are shown in figure 7. To obtain the significance for the model-independent results, the LMSE is calculated for each original and predicted scalogram pair, with the sum in eq. (11.1) running over all of the scalogram bins. An ensemble of LMSE values for the background prediction is then generated from many pairs of original and predicted scalograms. From this ensemble, the significance is calculated for the data scalogram. The significances for the model-independent results in each analysis channel are given in table 2. No significant deviations from the background-only hypothesis are observed. The largest deviation from the background-only (SM) hypothesis is found in the dielectron channel with the 50% threshold and the scale threshold, with an excess corresponding to a significance of 1.5σ. For the AE-based model-independent result, an alternative approach is also tested where the training is performed using syst-toys rather than stat-toys. In this approach, all steps of the AE-based search after the training match the procedure described in section 11.2. The significance values in this alternative approach are found to be comparable to the results given in table 2. The model-dependent results are also presented for each analysis channel. No significant deviation from the background-only hypothesis is seen in either of the analysis channels. Test statistic distributions assuming both a signal model, with k= 1033 GeV and M5= 9000 GeV, and the Standard Model are shown in figure 8and they are compared with the test statistic from the observed data. In the absence of a clear signal, limits at 95% CL are set on the CW/LD model in the k-M5plane. The limits for the case with mass thresholding, as discussed in section 11.1, are shown in figure 9. Additional exclusions are shown for the case without mass thresholds in figure 10. As expected, the sensitivity and corresponding limits are stronger for the case without mass thresholds in the region of k.1000 GeV. For the case with mass thresholds, the maximum excluded M5value in the diphoton channel is approximately 10TeV for values of k≈600 GeV, while the maximum excluded M5value in the dielectron channel is approximately 8.4 TeV for values of k≈900 GeV. For the case without mass thresholds, the maximum excluded M5value in the diphoton channel is approximately 11 TeV for values of k≈200 −1000 GeV, while the maximum excluded M5value in the dielectron channel is approximately 8.8TeV for values of k≈400 GeV. The areas in k-M5where the observed limits are stronger than the expected limits indicate that – 21 – JHEP10(2023)079 1000 2000 3000 4000 mee[GeV] 1 3 10 30 100 300 1000 [GeV] p s=13 TeV, 139 fb 1 ATLAS 0.00 0.25 0.50 0.75 1.00 1.25 1.50 1.75 2.00 |W( ,mee)| [GeV 1/2] (a) 500 1000 1500 2000 2500 m [GeV] 1 3 10 30 100 300 1000 [GeV] p s=13 TeV, 139 fb 1 ATLAS 0.00 0.25 0.50 0.75 1.00 1.25 1.50 1.75 2.00 |W( ,m )| [GeV 1/2] (b) Figure 6. The scalogram output of the CWT for the (a) dielectron and (b) diphoton search channels for the observed signal region data. The scalograms are shown here with a mass binning of 1GeV and are rebinned to a coarser binning before running the neural networks. Here αis the CWT scale parameter and W(α, β)are the wavelet coefficients defined in eq. (10.1), where the invariant mass of each channel takes the role of β. The range of mee and mγγ is chosen such that the plots cover approximately 300 GeV after the last observed data point. 500 1000 1500 2000 2500 mee[GeV] 1 3 10 30 100 300 1000 [GeV] p s=13 TeV, 139 fb 1 ATLAS 1 2 3 4 5 6 7 8 Negative logarithm of local p-value (a) 500 1000 1500 2000 m [GeV] 1 3 10 30 100 300 1000 [GeV] p s=13 TeV, 139 fb 1 ATLAS 1 2 3 4 5 6 Negative logarithm of local p-value (b) Figure 7. The negative logarithm of the local p-values calculated for the observed data mass distributions in the (a) dielectron and (b) diphoton search channels for the R < 50% scenario. Here αis the CWT scale parameter. – 22 – JHEP10(2023)079 Threshold Dielectron Significance Diphoton Significance R < 10% 0.4 –1.8 R < 50% 1.5 –0.2 No threshold 0.7 –0.7 Scale threshold 1.5 –0.6 Table 2. The observed significance in each analysis channel for the different thresholds considered in the model-independent analysis. Each significance is signed with positive (negative) values indicating the loss from data is higher (lower) than the median loss from the expected background. 0.0 0.2 0.4 0.6 0.8 1.0 t 0 2000 4000 6000 8000 Count ATLAS p s=13 TeV, 139 fb 1 Bkg with uncert. Bkg+Sig with uncert. Data channel k = 1033.0 GeV, M5 = 9000.0 GeV Figure 8. The distribution of the test statistic tµassuming a signal model µwith k= 1033 GeV and M5= 9000 GeV and assuming the Standard Model in the diphoton channel for the case where mass thresholds are applied. The pseudo-experiments used to generate the test statistic include the effects from the statistical uncertainty and all systematic uncertainties. The test statistic from observed data is shown as a dotted line. the data is effectively smoother than the expected fluctuations arising from the statistical and systematic uncertainties. The systematic uncertainties mainly impact the classifier result in the range of 500 < k < 1500 GeV for both of the analysis channels. The statistical uncertainties dominate in the higher kranges. The sensitivity of the classifier with systematic uncertainties is at most ∼1TeV weaker in M5exclusion than the limits evaluated without including the systematic uncertainties. The most dominant uncertainty contribution in both of the channels is due to the theoretical uncertainties in the background modelling. It is worthwhile to mention that the NNs are initially trained on a lattice of points in the k-M5plane, where a dedicated training is performed for each lattice point. The results are verified to be similarly effective for the models in-between those lattice points. – 23 – JHEP10(2023)079 (a) (b) Figure 9. The expected and observed exclusion limits at 95% CL for the clockwork gravity model projected in the k-M5parameter space for the (a) ee and (b) γγ channels, both for the case with mass thresholds. The surrounding shaded bands represent the ±1σand ±2σuncertainties on the expected limit. The shaded area with k > M5illustrates the region of parameter space where the CW/LD theory becomes non-perturbative. (a) (b) Figure 10. The expected and observed exclusion limits at 95% CL for the clockwork gravity model projected in the k-M5parameter space for the (a) ee and (b) γγ channels, both for the case without mass thresholds. The surrounding shaded bands represent the ±1σand ±2σuncertainties on the expected limit. The shaded area with k > M5illustrates the region of phase space where the CW/LD theory becomes non-perturbative. – 24 – JHEP10(2023)079 The ATLAS collaboration G. Aad 103, B. Abbott 121, K. Abeling 55, N.J. Abicht 49, S.H. Abidi 29, A. Aboulhorma 35e, H. Abramowicz 152, H. Abreu 151, Y. Abulaiti 118, A.C. Abusleme Hoffman 138a, B.S. Acharya 69a,69b,q, C. Adam Bourdarios 4, L. Adamczyk 86a, L. Adamek 156, S.V. Addepalli 26, M.J. Addison 102, J. Adelman 116, A. Adiguzel 21c, T. Adye 135, A.A. Affolder 137, Y. Afik 36, M.N. Agaras 13, J. Agarwala 73a,73b, A. Aggarwal 101, C. Agheorghiesei 27c, A. Ahmad 36, F. Ahmadov 38,ae, W.S. Ahmed 105, S. Ahuja 96, X. Ai 62a, G. Aielli 76a,76b, M. Ait Tamlihat 35e, B. Aitbenchikh 35a, I. Aizenberg 170, M. Akbiyik 101, T.P.A. Åkesson 99, A.V. Akimov 37, D. Akiyama 169, N.N. Akolkar 24, K. Al Khoury 41, G.L. Alberghi 23b, J. Albert 166, P. Albicocco 53, G.L. Albouy 60, S. Alderweireldt 52, M. Aleksa 36, I.N. Aleksandrov 38, C. Alexa 27b, T. Alexopoulos 10, A. Alfonsi 115, F. Alfonsi 23b, M. Algren 56, M. Alhroob 121, B. Ali 133, H.M.J. Ali 92, S. Ali 149, S.W. Alibocus 93, M. Aliev 37, G. Alimonti 71a, W. Alkakhi 55, C. Allaire 66, B.M.M. Allbrooke 147, J.F. Allen 52, C.A. Allendes Flores 138f, P.P. Allport 20, A. Aloisio 72a,72b, F. Alonso 91, C. Alpigiani 139, M. Alvarez Estevez 100, A. Alvarez Fernandez 101, M.G. Alviggi 72a,72b, M. Aly 102, Y. Amaral Coutinho 83b, A. Ambler 105, C. Amelung36, M. Amerl 102, C.G. Ames 110, D. Amidei 107, S.P. Amor Dos Santos 131a, K.R. Amos 164, V. Ananiev 126, C. Anastopoulos 140, T. Andeen 11, J.K. Anders 36, S.Y. Andrean 47a,47b, A. Andreazza 71a,71b, S. Angelidakis 9, A. Angerami 41,ah, A.V. Anisenkov 37, A. Annovi 74a, C. Antel 56, M.T. Anthony 140, E. Antipov 146, M. Antonelli 53, D.J.A. Antrim 17a, F. Anulli 75a, M. Aoki 84, T. Aoki 154, J.A. Aparisi Pozo 164, M.A. Aparo 147, L. Aperio Bella 48, C. Appelt 18, A. Apyan 26, N. Aranzabal 36, C. Arcangeletti 53, A.T.H. Arce 51, E. Arena 93, J-F. Arguin 109, S. Argyropoulos 54, J.-H. Arling 48, A.J. Armbruster 36, O. Arnaez 4, H. Arnold 115, Z.P. Arrubarrena Tame110, G. Artoni 75a,75b, H. Asada 112, K. Asai 119, S. Asai 154, N.A. Asbah 61, J. Assahsah 35d, K. Assamagan 29, R. Astalos 28a, S. Atashi 161, R.J. Atkin 33a, M. Atkinson163, N.B. Atlay 18, H. Atmani62b, P.A. Atmasiddha 107, K. Augsten 133, S. Auricchio 72a,72b, A.D. Auriol 20, V.A. Austrup 102, G. Avolio 36, K. Axiotis 56, G. Azuelos 109,al, D. Babal 28b, H. Bachacou 136, K. Bachas 153,u, A. Bachiu 34, F. Backman 47a,47b, A. Badea 61, P. Bagnaia 75a,75b, M. Bahmani 18, A.J. Bailey 164, V.R. Bailey 163, J.T. Baines 135, L. Baines 95, C. Bakalis 10, O.K. Baker 173, E. Bakos 15, D. Bakshi Gupta 8, R. Balasubramanian 115, E.M. Baldin 37, P. Balek 86a, E. Ballabene 23b,23a, F. Balli 136, L.M. Baltes 63a, W.K. Balunas 32, J. Balz 101, E. Banas 87, M. Bandieramonte 130, A. Bandyopadhyay 24, S. Bansal 24, L. Barak 152, M. Barakat 48, E.L. Barberio 106, D. Barberis 57b,57a, M. Barbero 103, G. Barbour97, K.N. Barends 33a, T. Barillari 111, M-S. Barisits 36, T. Barklow 144, P. Baron 123, D.A. Baron Moreno 102, A. Baroncelli 62a, G. Barone 29, A.J. Barr 127, J.D. Barr 97, L. Barranco Navarro 47a,47b, F. Barreiro 100, J. Barreiro Guimarães da Costa 14a, U. Barron 152, M.G. Barros Teixeira 131a, S. Barsov 37, F. Bartels 63a, R. Bartoldus 144, A.E. Barton 92, P. Bartos 28a, A. Basan 101, M. Baselga 49, A. Bassalat 66,b, M.J. Basso 157a, C.R. Basson 102, R.L. Bates 59, – 31 – JHEP10(2023)079 S. Batlamous35e, J.R. Batley 32, B. Batool 142, M. Battaglia 137, D. Battulga 18, M. Bauce 75a,75b, M. Bauer 36, P. Bauer 24, L.T. Bazzano Hurrell 30, J.B. Beacham 51, T. Beau 128, P.H. Beauchemin 159, H. Beauchesnen, F. Becherer 54, P. Bechtle 24, H.P. Beck 19,t, K. Becker 168, A.J. Beddall 82, V.A. Bednyakov 38, C.P. Bee 146, L.J. Beemster15, T.A. Beermann 36, M. Begalli 83d, M. Begel 29, A. Behera 146, J.K. Behr 48, J.F. Beirer 55, F. Beisiegel 24, M. Belfkir 160, G. Bella 152, L. Bellagamba 23b, A. Bellerive 34, P. Bellos 20, K. Beloborodov 37, N.L. Belyaev 37, D. Benchekroun 35a, F. Bendebba 35a, Y. Benhammou 152, M. Benoit 29, J.R. Bensinger 26, S. Bentvelsen 115, L. Beresford 48, M. Beretta 53, E. Bergeaas Kuutmann 162, N. Berger 4, B. Bergmann 133, J. Beringer 17a, G. Bernardi 5, C. Bernius 144, F.U. Bernlochner 24, F. Bernon 36,103, T. Berry 96, P. Berta 134, A. Berthold 50, I.A. Bertram 92, S. Bethke 111, A. Betti 75a,75b, A.J. Bevan 95, M. Bhamjee 33c, S. Bhatta 146, D.S. Bhattacharya 167, P. Bhattarai 26, V.S. Bhopatkar 122, R. Bi29,an, R.M. Bianchi 130, G. Bianco 23b,23a, O. Biebel 110, R. Bielski 124, M. Biglietti 77a, T.R.V. Billoud 133, M. Bindi 55, A. Bingul 21b, C. Bini 75a,75b, A. Biondini 93, C.J. Birch-sykes 102, G.A. Bird 20,135, M. Birman 170, M. Biros 134, T. Bisanz 49, E. Bisceglie 43b,43a, D. Biswas 142, A. Bitadze 102, K. Bjørke 126, I. Bloch 48, C. Blocker 26, A. Blue 59, U. Blumenschein 95, J. Blumenthal 101, G.J. Bobbink 115, V.S. Bobrovnikov 37, M. Boehler 54, B. Boehm 167, D. Bogavac 36, A.G. Bogdanchikov 37, C. Bohm 47a, V. Boisvert 96, P. Bokan 48, T. Bold 86a, M. Bomben 5, M. Bona 95, M. Boonekamp 136, C.D. Booth 96, A.G. Borbély 59, I.S. Bordulev 37, H.M. Borecka-Bielska 109, L.S. Borgna 97, G. Borissov 92, D. Bortoletto 127, D. Boscherini 23b, M. Bosman 13, J.D. Bossio Sola 36, K. Bouaouda 35a, N. Bouchhar 164, J. Boudreau 130, E.V. Bouhova-Thacker 92, D. Boumediene 40, R. Bouquet 5, A. Boveia 120, J. Boyd 36, D. Boye 29, I.R. Boyko 38, J. Bracinik 20, N. Brahimi 62d, G. Brandt 172, O. Brandt 32, F. Braren 48, B. Brau 104, J.E. Brau 124, R. 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Calvet 103, M. Calvetti 74a,74b, R. Camacho Toro 128, S. Camarda 36, D. Camarero Munoz 26, P. Camarri 76a,76b, M.T. Camerlingo 72a,72b, D. Cameron 126, C. Camincher 166, M. Campanelli 97, A. Camplani 42, V. Canale 72a,72b, A. Canesse 105, M. Cano Bret 80, J. Cantero 164, Y. Cao 163, F. Capocasa 26, M. Capua 43b,43a, A. Carbone 71a,71b, R. Cardarelli 76a, J.C.J. Cardenas 8, F. Cardillo 164, T. Carli 36, G. Carlino 72a, J.I. Carlotto 13, – 32 – JHEP10(2023)079 B.T. Carlson 130,v, E.M. Carlson 166,157a, L. Carminati 71a,71b, A. Carnelli 136, M. Carnesale 75a,75b, S. Caron 114, E. Carquin 138f, S. Carrá 71a,71b, G. Carratta 23b,23a, F. Carrio Argos 33g, J.W.S. Carter 156, T.M. Carter 52, M.P. Casado 13,j, M. Caspar 48, E.G. Castiglia 173, F.L. Castillo 4, L. Castillo Garcia 13, V. Castillo Gimenez 164, N.F. Castro 131a,131e, A. Catinaccio 36, J.R. Catmore 126, V. Cavaliere 29, N. Cavalli 23b,23a, V. Cavasinni 74a,74b, Y.C. Cekmecelioglu 48, E. Celebi 21a, F. Celli 127, M.S. Centonze 70a,70b, K. Cerny 123, A.S. Cerqueira 83a, A. Cerri 147, L. Cerrito 76a,76b, F. Cerutti 17a, B. Cervato 142, A. Cervelli 23b, G. Cesarini 53, S.A. Cetin 82, Z. Chadi 35a, D. Chakraborty 116, M. Chala 131f, J. Chan 171, W.Y. Chan 154, J.D. Chapman 32, E. Chapon 136, B. Chargeishvili 150b, D.G. Charlton 20, T.P. Charman 95, M. Chatterjee 19, C. Chauhan 134, S. Chekanov 6, S.V. Chekulaev 157a, G.A. Chelkov 38,a, A. Chen 107, B. Chen 152, B. Chen 166, H. Chen 14c, H. Chen 29, J. Chen 62c, J. Chen 143, M. Chen 127, S. Chen 154, S.J. Chen 14c, X. Chen 62c, X. Chen 14b,ak, Y. Chen 62a, C.L. Cheng 171, H.C. Cheng 64a, S. Cheong 144, A. Cheplakov 38, E. Cheremushkina 48, E. Cherepanova 115, R. Cherkaoui El Moursli 35e, E. Cheu 7, K. Cheung 65, L. Chevalier 136, V. Chiarella 53, G. Chiarelli 74a, N. Chiedde 103, G. Chiodini 70a, A.S. Chisholm 20, A. Chitan 27b, M. Chitishvili 164, M.V. Chizhov 38, K. Choi 11, A.R. Chomont 75a,75b, Y. Chou 104, E.Y.S. Chow 115, T. Chowdhury 33g, K.L. Chu170, M.C. Chu 64a, X. Chu 14a,14e, J. Chudoba 132, J.J. Chwastowski 87, D. Cieri 111, K.M. Ciesla 86a, V. Cindro 94, A. Ciocio 17a, F. Cirotto 72a,72b, Z.H. Citron 170,o, M. Citterio 71a, D.A. Ciubotaru27b, B.M. Ciungu 156, A. Clark 56, P.J. Clark 52, J.M. Clavijo Columbie 48, S.E. Clawson 48, C. Clement 47a,47b, J. Clercx 48, L. Clissa 23b,23a, Y. Coadou 103, M. Cobal 69a,69c, A. Coccaro 57b, R.F. Coelho Barrue 131a, R. Coelho Lopes De Sa 104, S. Coelli 71a, H. Cohen 152, A.E.C. Coimbra 71a,71b, B. Cole 41, J. Collot 60, P. Conde Muiño 131a,131g, M.P. Connell 33c, S.H. Connell 33c, I.A. Connelly 59, E.I. Conroy 127, F. Conventi 72a,am, H.G. Cooke 20, A.M. Cooper-Sarkar 127, A. Cordeiro Oudot Choi 128, F. Cormier 165, L.D. Corpe 40, M. Corradi 75a,75b, F. Corriveau 105,ac, A. Cortes-Gonzalez 18, M.J. Costa 164, F. Costanza 4, D. Costanzo 140, B.M. Cote 120, G. Cowan 96, K. Cranmer 171, D. Cremonini 23b,23a, S. Crépé-Renaudin 60, F. Crescioli 128, M. Cristinziani 142, M. Cristoforetti 78a,78b, V. Croft 115, J.E. Crosby 122, G. Crosetti 43b,43a, A. Cueto 100, T. Cuhadar Donszelmann 161, H. Cui 14a,14e, Z. Cui 7, W.R. Cunningham 59, F. Curcio 43b,43a, P. Czodrowski 36, M.M. Czurylo 63b, M.J. Da Cunha Sargedas De Sousa 62a, J.V. Da Fonseca Pinto 83b, C. Da Via 102, W. Dabrowski 86a, T. Dado 49, S. Dahbi 33g, T. Dai 107, C. Dallapiccola 104, M. Dam 42, G. D’amen 29, V. D’Amico 110, J. Damp 101, J.R. Dandoy 129, M.F. Daneri 30, M. Danninger 143, V. Dao 36, G. Darbo 57b, S. Darmora 6, S.J. Das 29,an, S. D’Auria 71a,71b, C. David 157b, T. Davidek 134, B. Davis-Purcell 34, I. Dawson 95, H.A. Day-hall 133, K. De 8, R. De Asmundis 72a, N. De Biase 48, S. De Castro 23b,23a, N. De Groot 114, P. de Jong 115, H. De la Torre 108, A. De Maria 14c, A. De Salvo 75a, U. De Sanctis 76a,76b, A. De Santo 147, J.B. De Vivie De Regie 60, D.V. Dedovich38, J. Degens 115, A.M. Deiana 44, F. Del Corso 23b,23a, J. Del Peso 100, F. Del Rio 63a, F. Deliot 136, C.M. Delitzsch 49, M. Della Pietra 72a,72b, D. Della Volpe 56, A. Dell’Acqua 36, L. Dell’Asta 71a,71b, M. Delmastro 4, P.A. Delsart 60, S. Demers 173, – 33 – JHEP10(2023)079 M. Demichev 38, S.P. Denisov 37, L. D’Eramo 40, D. Derendarz 87, F. Derue 128, P. Dervan 93, K. Desch 24, C. Deutsch 24, F.A. Di Bello 57b,57a, A. Di Ciaccio 76a,76b, L. Di Ciaccio 4, A. Di Domenico 75a,75b, C. Di Donato 72a,72b, A. Di Girolamo 36, G. Di Gregorio 5, A. Di Luca 78a,78b, B. Di Micco 77a,77b, R. Di Nardo 77a,77b, C. Diaconu 103, F.A. Dias 115, T. Dias Do Vale 143, M.A. Diaz 138a,138b, F.G. Diaz Capriles 24, M. Didenko 164, E.B. Diehl 107, L. Diehl 54, S. Díez Cornell 48, C. Diez Pardos 142, C. Dimitriadi 24,162, A. Dimitrievska 17a, J. Dingfelder 24, I-M. Dinu 27b, S.J. Dittmeier 63b, F. Dittus 36, F. Djama 103, T. Djobava 150b, J.I. Djuvsland 16, C. Doglioni 102,99, J. Dolejsi 134, Z. Dolezal 134, M. Donadelli 83c, B. Dong 108, J. Donini 40, A. D’Onofrio 77a,77b, M. D’Onofrio 93, J. Dopke 135, A. Doria 72a, N. Dos Santos Fernandes 131a, M.T. Dova 91, A.T. Doyle 59, M.A. Draguet 127, E. Dreyer 170, I. Drivas-koulouris 10, A.S. Drobac 159, M. Drozdova 56, D. Du 62a, T.A. du Pree 115, F. Dubinin 37, M. Dubovsky 28a, E. Duchovni 170, G. Duckeck 110, O.A. Ducu 27b, D. Duda 52, A. Dudarev 36, E.R. Duden 26, M. D’uffizi 102, L. Duflot 66, M. Dührssen 36, C. Dülsen 172, A.E. Dumitriu 27b, M. Dunford 63a, S. Dungs 49, K. Dunne 47a,47b, A. Duperrin 103, H. Duran Yildiz 3a, M. Düren 58, A. Durglishvili 150b, B.L. Dwyer 116, G.I. Dyckes 17a, M. Dyndal 86a, S. Dysch 102, B.S. Dziedzic 87, Z.O. Earnshaw 147, G.H. Eberwein 127, B. Eckerova 28a, S. Eggebrecht 55, M.G. Eggleston51, E. Egidio Purcino De Souza 128, L.F. Ehrke 56, G. Eigen 16, K. Einsweiler 17a, T. Ekelof 162, P.A. Ekman 99, S. El Farkh 35b, Y. El Ghazali 35b, H. El Jarrari 35e,149, A. El Moussaouy 35a, V. Ellajosyula 162, M. Ellert 162, F. Ellinghaus 172, A.A. Elliot 95, N. Ellis 36, J. Elmsheuser 29, M. Elsing 36, D. Emeliyanov 135, Y. Enari 154, I. Ene 17a, S. Epari 13, J. Erdmann 49, P.A. Erland 87, M. Errenst 172, M. Escalier 66, C. Escobar 164, E. Etzion 152, G. Evans 131a, H. Evans 68, L.S. Evans 96, M.O. Evans 147, A. Ezhilov 37, S. Ezzarqtouni 35a, F. Fabbri 59, L. Fabbri 23b,23a, G. Facini 97, V. Fadeyev 137, R.M. Fakhrutdinov 37, S. Falciano 75a, L.F. Falda Ulhoa Coelho 36, P.J. Falke 24, J. Faltova 134, C. Fan 163, Y. Fan 14a, Y. Fang 14a,14e, M. Fanti 71a,71b, M. Faraj 69a,69b, Z. Farazpay98, A. Farbin 8, A. Farilla 77a, T. Farooque 108, S.M. Farrington 52, F. Fassi 35e, D. Fassouliotis 9, M. Faucci Giannelli 76a,76b, W.J. Fawcett 32, L. Fayard 66, P. Federic 134, P. Federicova 132, O.L. Fedin 37,a, G. Fedotov 37, M. Feickert 171, L. Feligioni 103, D.E. Fellers 124, C. Feng 62b, M. Feng 14b, Z. Feng 115, M.J. Fenton 161, A.B. Fenyuk37, L. Ferencz 48, R.A.M. Ferguson 92, S.I. Fernandez Luengo 138f, M.J.V. Fernoux 103, J. Ferrando 48, A. Ferrari 162, P. Ferrari 115,114, R. Ferrari 73a, D. Ferrere 56, C. Ferretti 107, F. Fiedler 101, A. Filipčič 94, E.K. Filmer 1, F. Filthaut 114, M.C.N. Fiolhais 131a,131c,d, L. Fiorini 164, W.C. Fisher 108, T. Fitschen 102, P.M. Fitzhugh136, I. Fleck 142, P. Fleischmann 107, T. Flick 172, L. Flores 129, M. Flores 33d,ai, L.R. Flores Castillo 64a, L. Flores Sanz De Acedo 36, F.M. Follega 78a,78b, N. Fomin 16, J.H. Foo 156, B.C. Forland68, A. Formica 136, A.C. Forti 102, E. Fortin 36, A.W. Fortman 61, M.G. Foti 17a, L. Fountas 9,k, D. Fournier 66, H. Fox 92, P. Francavilla 74a,74b, S. Francescato 61, S. Franchellucci 56, M. Franchini 23b,23a, S. Franchino 63a, D. Francis36, L. Franco 114, L. Franconi 48, M. Franklin 61, G. Frattari 26, A.C. Freegard 95, W.S. Freund 83b, Y.Y. Frid 152, N. Fritzsche 50, A. Froch 54, D. Froidevaux 36, J.A. Frost 127, Y. Fu 62a, – 34 – JHEP10(2023)079 M. Fujimoto 119, E. Fullana Torregrosa 164,∗, K.Y. Fung 64a, E. Furtado De Simas Filho 83b, M. Furukawa 154, J. Fuster 164, A. Gabrielli 23b,23a, A. Gabrielli 156, P. Gadow 48, G. Gagliardi 57b,57a, L.G. Gagnon 17a, E.J. Gallas 127, B.J. Gallop 135, K.K. Gan 120, S. Ganguly 154, J. Gao 62a, Y. Gao 52, F.M. Garay Walls 138a,138b, B. Garcia29,an, C. García 164, A. Garcia Alonso 115, A.G. Garcia Caffaro 173, J.E. García Navarro 164, M. Garcia-Sciveres 17a, G.L. Gardner 129, R.W. Gardner 39, N. Garelli 159, D. Garg 80, R.B. Garg 144,s, J.M. Gargan52, C.A. Garner156, S.J. Gasiorowski 139, P. Gaspar 83b, G. Gaudio 73a, V. Gautam13, P. Gauzzi 75a,75b, I.L. Gavrilenko 37, A. Gavrilyuk 37, C. Gay 165, G. Gaycken 48, E.N. Gazis 10, A.A. Geanta 27b, C.M. Gee 137, C. Gemme 57b, M.H. Genest 60, S. Gentile 75a,75b, S. George 96, W.F. George 20, T. Geralis 46, P. Gessinger-Befurt 36, M.E. Geyik 172, M. Ghneimat 142, K. Ghorbanian 95, A. Ghosal 142, A. Ghosh 161, A. Ghosh 7, B. Giacobbe 23b, S. Giagu 75a,75b, P. Giannetti 74a, A. Giannini 62a, S.M. Gibson 96, M. Gignac 137, D.T. Gil 86b, A.K. Gilbert 86a, B.J. Gilbert 41, D. Gillberg 34, G. Gilles 115, N.E.K. Gillwald 48, L. Ginabat 128, D.M. Gingrich 2,al, M.P. Giordani 69a,69c, P.F. Giraud 136, G. Giugliarelli 69a,69c, D. Giugni 71a, F. Giuli 36, I. Gkialas 9,k, L.K. Gladilin 37, C. Glasman 100, G.R. Gledhill 124, M. Glisic124, I. Gnesi 43b,g, Y. Go 29,an, M. Goblirsch-Kolb 36, B. Gocke 49, D. Godin109, B. Gokturk 21a, S. Goldfarb 106, T. Golling 56, M.G.D. Gololo33g, D. Golubkov 37, J.P. Gombas 108, A. Gomes 131a,131b, G. Gomes Da Silva 142, A.J. Gomez Delegido 164, R. Gonçalo 131a,131c, G. Gonella 124, L. Gonella 20, A. Gongadze 38, F. Gonnella 20, J.L. Gonski 41, R.Y. González Andana 52, S. González de la Hoz 164, S. Gonzalez Fernandez 13, R. Gonzalez Lopez 93, C. Gonzalez Renteria 17a, R. Gonzalez Suarez 162, S. Gonzalez-Sevilla 56, G.R. Gonzalvo Rodriguez 164, L. Goossens 36, P.A. Gorbounov 37, B. Gorini 36, E. Gorini 70a,70b, A. Gorišek 94, T.C. Gosart 129, A.T. Goshaw 51, M.I. Gostkin 38, S. Goswami 122, C.A. Gottardo 36, M. Gouighri 35b, V. Goumarre 48, A.G. Goussiou 139, N. Govender 33c, I. Grabowska-Bold 86a, K. Graham 34, E. Gramstad 126, S. Grancagnolo 70a,70b, M. Grandi 147, V. Gratchev37,∗, P.M. Gravila 27f, F.G. Gravili 70a,70b, H.M. Gray 17a, M. Greco 70a,70b, C. Grefe 24, I.M. Gregor 48, P. Grenier 144, C. Grieco 13, A.A. Grillo 137, K. Grimm 31, S. Grinstein 13,y, J.-F. Grivaz 66, E. Gross 170, J. Grosse-Knetter 55, C. Grud107, J.C. Grundy 127, L. Guan 107, W. Guan 171, C. Gubbels 165, J.G.R. Guerrero Rojas 164, G. Guerrieri 69a,69b, F. Guescini 111, R. Gugel 101, J.A.M. Guhit 107, A. Guida 18, T. Guillemin 4, E. Guilloton 168,135, S. Guindon 36, F. Guo 14a,14e, J. Guo 62c, L. Guo 48, Y. Guo 107, R. Gupta 48, S. Gurbuz 24, S.S. Gurdasani 54, G. Gustavino 36, M. Guth 56, P. Gutierrez 121, L.F. Gutierrez Zagazeta 129, C. Gutschow 97, C. Gwenlan 127, C.B. Gwilliam 93, E.S. Haaland 126, A. Haas 118, M. Habedank 48, C. Haber 17a, H.K. Hadavand 8, A. Hadef 101, S. Hadzic 111, J.J. Hahn 142, E.H. Haines 97, M. Haleem 167, J. Haley 122, J.J. Hall 140, G.D. Hallewell 103, L. Halser 19, K. Hamano 166, H. Hamdaoui 35e, M. Hamer 24, G.N. Hamity 52, E.J. Hampshire 96, J. Han 62b, K. Han 62a, L. Han 14c, L. Han 62a, S. Han 17a, Y.F. Han 156, K. Hanagaki 84, M. Hance 137, D.A. Hangal 41,ah, H. Hanif 143, M.D. Hank 129, R. Hankache 102, J.B. Hansen 42, J.D. Hansen 42, P.H. Hansen 42, K. Hara 158, – 35 – JHEP10(2023)079 D. Harada 56, T. Harenberg 172, S. Harkusha 37, M.L. Harris 104, Y.T. Harris 127, J. Harrison 13, N.M. Harrison 120, P.F. Harrison168, N.M. Hartman 144, N.M. Hartmann 110, Y. Hasegawa 141, A. Hasib 52, S. Haug 19, R. Hauser 108, C.M. Hawkes 20, R.J. Hawkings 36, Y. Hayashi 154, S. Hayashida 112, D. Hayden 108, C. Hayes 107, R.L. Hayes 115, C.P. Hays 127, J.M. Hays 95, H.S. Hayward 93, F. He 62a, M. He 14a,14e, Y. He 155, Y. He 128, N.B. Heatley 95, V. Hedberg 99, A.L. Heggelund 126, N.D. Hehir 95, C. Heidegger 54, K.K. Heidegger 54, W.D. Heidorn 81, J. Heilman 34, S. Heim 48, T. Heim 17a, J.G. Heinlein 129, J.J. Heinrich 124, L. Heinrich 111,aj , J. Hejbal 132, L. Helary 48, A. Held 171, S. Hellesund 16, C.M. Helling 165, S. Hellman 47a,47b, C. Helsens 36, R.C.W. Henderson92, L. Henkelmann 32, A.M. Henriques Correia36, H. Herde 99, Y. Hernández Jiménez 146, L.M. Herrmann 24, T. Herrmann 50, G. Herten 54, R. Hertenberger 110, L. Hervas 36, M.E. Hesping 101, N.P. Hessey 157a, H. Hibi 85, S.J. Hillier 20, J.R. Hinds 108, F. Hinterkeuser 24, M. Hirose 125, S. Hirose 158, D. Hirschbuehl 172, T.G. Hitchings 102, B. Hiti 94, J. Hobbs 146, R. Hobincu 27e, N. Hod 170, M.C. Hodgkinson 140, B.H. Hodkinson 32, A. Hoecker 36, J. Hofer 48, T. Holm 24, M. Holzbock 111, L.B.A.H. Hommels 32, B.P. Honan 102, J. Hong 62c, T.M. Hong 130, B.H. Hooberman 163, W.H. Hopkins 6, Y. Horii 112, S. Hou 149, A.S. Howard 94, J. Howarth 59, J. Hoya 6, M. Hrabovsky 123, A. Hrynevich 48, T. Hryn’ova 4, P.J. Hsu 65, S.-C. Hsu 139, Q. Hu 41, Y.F. Hu 14a,14e, S. Huang 64b, X. Huang 14c, Y. Huang 62a, Y. Huang 14a, Z. Huang 102, Z. Hubacek 133, M. Huebner 24, F. Huegging 24, T.B. Huffman 127, C.A. Hugli 48, M. Huhtinen 36, S.K. Huiberts 16, R. Hulsken 105, N. Huseynov 12,a, J. Huston 108, J. Huth 61, R. Hyneman 144, G. Iacobucci 56, G. Iakovidis 29, I. Ibragimov 142, L. Iconomidou-Fayard 66, P. Iengo 72a,72b, R. Iguchi 154, T. Iizawa 84, Y. Ikegami 84, N. Ilic 156, H. Imam 35a, M. Ince Lezki 56, T. Ingebretsen Carlson 47a,47b, G. Introzzi 73a,73b, M. Iodice 77a, V. Ippolito 75a,75b, R.K. Irwin 93, M. Ishino 154, W. Islam 171, C. Issever 18,48, S. Istin 21a,ap, H. Ito 169, J.M. Iturbe Ponce 64a, R. Iuppa 78a,78b, A. Ivina 170, J.M. Izen 45, V. Izzo 72a, P. Jacka 132,133, P. Jackson 1, R.M. Jacobs 48, B.P. Jaeger 143, C.S. Jagfeld 110, P. Jain 54, G. Jäkel 172, K. Jakobs 54, T. Jakoubek 170, J. Jamieson 59, K.W. Janas 86a, A.E. Jaspan 93, M. Javurkova 104, F. Jeanneau 136, L. Jeanty 124, J. Jejelava 150a,af , P. Jenni 54,h, C.E. Jessiman 34, S. Jézéquel 4, C. Jia62b, J. Jia 146, X. Jia 61, X. Jia 14a,14e, Z. Jia 14c, Y. Jiang62a, S. Jiggins 48, J. Jimenez Pena 13, S. Jin 14c, A. Jinaru 27b, O. Jinnouchi 155, P. Johansson 140, K.A. Johns 7, J.W. Johnson 137, D.M. Jones 32, E. Jones 48, P. Jones 32, R.W.L. Jones 92, T.J. Jones 93, R. Joshi 120, J. Jovicevic 15, X. Ju 17a, J.J. Junggeburth 36, T. Junkermann 63a, A. Juste Rozas 13,y, M.K. Juzek 87, S. Kabana 138e, A. Kaczmarska 87, M. Kado 111, H. Kagan 120, M. Kagan 144, A. Kahn41, A. Kahn 129, C. Kahra 101, T. Kaji 169, E. Kajomovitz 151, N. Kakati 170, I. Kalaitzidou 54, C.W. Kalderon 29, A. Kamenshchikov 156, S. Kanayama 155, N.J. Kang 137, D. Kar 33g, K. Karava 127, M.J. Kareem 157b, E. Karentzos 54, I. Karkanias 153, O. Karkout 115, S.N. Karpov 38, Z.M. Karpova 38, V. Kartvelishvili 92, A.N. Karyukhin 37, E. Kasimi 153, Y. Katsn, J. Katzy 48, S. Kaur 34, K. Kawade 141, T. Kawamoto 136, E.F. Kay 36, F.I. Kaya 159, S. Kazakos 108, V.F. Kazanin 37, Y. Ke 146, J.M. Keaveney 33a, R. Keeler 166, – 36 – JHEP10(2023)079 G.V. Kehris 61, J.S. Keller 34, A.S. Kelly97, J.J. Kempster 147, K.E. Kennedy 41, P.D. Kennedy 101, O. Kepka 132, B.P. Kerridge 168, S. Kersten 172, B.P. Kerševan 94, S. Keshri 66, L. Keszeghova 28a, S. Ketabchi Haghighat 156, M. Khandoga 128, A. Khanov 122, A.G. Kharlamov 37, T. Kharlamova 37, E.E. Khoda 139, T.J. Khoo 18, G. Khoriauli 167, J. Khubua 150b, Y.A.R. Khwaira 66, M. Kiehn 36, A. Kilgallon 124, D.W. Kim 47a,47b, Y.K. Kim 39, N. Kimura 97, A. Kirchhoff 55, C. Kirfel 24, F. Kirfel 24, J. Kirk 135, A.E. Kiryunin 111, C. Kitsaki 10, O. Kivernyk 24, M. Klassen 63a, C. Klein 34, L. Klein 167, M.H. Klein 107, M. Klein 93, S.B. Klein 56, U. Klein 93, P. Klimek 36, A. Klimentov 29, T. Klioutchnikova 36, P. Kluit 115, S. Kluth 111, E. Kneringer 79, T.M. Knight 156, A. Knue 54, R. Kobayashi 88, S.F. Koch 127, M. Kocian 144, P. Kodyš 134, D.M. Koeck 124, P.T. Koenig 24, T. Koffas 34, M. Kolb 136, I. Koletsou 4, T. Komarek 123, K. Köneke 54, A.X.Y. Kong 1, T. Kono 119, N. Konstantinidis 97, B. Konya 99, R. Kopeliansky 68, S. Koperny 86a, K. Korcyl 87, K. Kordas 153,f , G. Koren 152, A. Korn 97, S. Korn 55, I. Korolkov 13, N. Korotkova 37, B. Kortman 115, O. Kortner 111, S. Kortner 111, W.H. Kostecka 116, V.V. Kostyukhin 142, A. Kotsokechagia 136, A. Kotwal 51, A. Koulouris 36, A. Kourkoumeli-Charalampidi 73a,73b, C. Kourkoumelis 9, E. Kourlitis 6, O. Kovanda 147, R. Kowalewski 166, W. Kozanecki 136, A.S. Kozhin 37, V.A. Kramarenko 37, G. Kramberger 94, P. Kramer 101, M.W. Krasny 128, A. Krasznahorkay 36, J.W. Kraus 172, J.A. Kremer 101, T. Kresse 50, J. Kretzschmar 93, K. Kreul 18, P. Krieger 156, S. Krishnamurthy 104, M. Krivos 134, K. Krizka 20, K. Kroeninger 49, H. Kroha 111, J. Kroll 132, J. Kroll 129, K.S. Krowpman 108, U. Kruchonak 38, H. Krüger 24, N. Krumnack81, M.C. Kruse 51, J.A. Krzysiak 87, O. Kuchinskaia 37, S. Kuday 3a, S. Kuehn 36, R. Kuesters 54, T. Kuhl 48, V. Kukhtin 38, Y. Kulchitsky 37,a, S. Kuleshov 138d,138b, M. Kumar 33g, N. Kumari 103, A. Kupco 132, T. Kupfer49, A. Kupich 37, O. Kuprash 54, H. Kurashige 85, L.L. Kurchaninov 157a, O. Kurdysh 66, Y.A. Kurochkin 37, A. Kurova 37, M. Kuze 155, A.K. Kvam 104, J. Kvita 123, T. Kwan 105, N.G. Kyriacou 107, L.A.O. Laatu 103, C. Lacasta 164, F. Lacava 75a,75b, H. Lacker 18, D. Lacour 128, N.N. Lad 97, E. Ladygin 38, B. Laforge 128, T. Lagouri 138e, S. Lai 55, I.K. Lakomiec 86a, N. Lalloue 60, J.E. Lambert 166,m, S. Lammers 68, W. Lampl 7, C. Lampoudis 153,f , A.N. Lancaster 116, E. Lançon 29, U. Landgraf 54, M.P.J. Landon 95, V.S. Lang 54, R.J. Langenberg 104, O.K.B. Langrekken 126, A.J. Lankford 161, F. Lanni 36, K. Lantzsch 24, A. Lanza 73a, A. Lapertosa 57b,57a, J.F. Laporte 136, T. Lari 71a, F. Lasagni Manghi 23b, M. Lassnig 36, V. Latonova 132, A. Laudrain 101, A. Laurier 151, S.D. Lawlor 96, Z. Lawrence 102, M. Lazzaroni 71a,71b, B. Le102, E.M. Le Boulicaut 51, B. Leban 94, A. Lebedev 81, M. LeBlanc 36, F. Ledroit-Guillon 60, A.C.A. Lee97, S.C. Lee 149, S. Lee 47a,47b, T.F. Lee 93, L.L. Leeuw 33c, H.P. Lefebvre 96, M. Lefebvre 166, C. Leggett 17a, G. Lehmann Miotto 36, M. Leigh 56, W.A. Leight 104, W. Leinonen 114, A. Leisos 153,x, M.A.L. Leite 83c, C.E. Leitgeb 48, R. Leitner 134, K.J.C. Leney 44, T. Lenz 24, S. Leone 74a, C. Leonidopoulos 52, A. Leopold 145, C. Leroy 109, R. Les 108, C.G. Lester 32, M. Levchenko 37, J. Levêque 4, D. Levin 107, L.J. Levinson 170, M.P. Lewicki 87, D.J. Lewis 4, A. Li 5, B. Li 62b, C. Li62a, C-Q. Li 62c, H. Li 62a, H. Li 62b, H. Li 14c, H. Li 62b, K. Li 139, L. Li 62c, M. Li 14a,14e, Q.Y. Li 62a, – 37 – JHEP10(2023)079 S. Li 14a,14e, S. Li 62d,62c,e, T. Li 5,c, X. Li 105, Z. Li 127, Z. Li 105, Z. Li 93, Z. Li 14a,14e, Z. Liang 14a, M. Liberatore 48, B. Liberti 76a, K. Lie 64c, J. Lieber Marin 83b, H. Lien 68, K. Lin 108, R.E. Lindley 7, J.H. Lindon 2, A. Linss 48, E. Lipeles 129, A. Lipniacka 16, A. Lister 165, J.D. Little 4, B. Liu 14a, B.X. Liu 143, D. Liu 62d,62c, J.B. Liu 62a, J.K.K. Liu 32, K. Liu 62d,62c, M. Liu 62a, M.Y. Liu 62a, P. Liu 14a, Q. Liu 62d,139,62c, X. Liu 62a, Y. Liu 14d,14e, Y.L. Liu 107, Y.W. Liu 62a, J. Llorente Merino 143, S.L. Lloyd 95, E.M. Lobodzinska 48, P. Loch 7, S. Loffredo 76a,76b, T. Lohse 18, K. Lohwasser 140, E. Loiacono 48, M. Lokajicek 132,∗, J.D. Lomas 20, J.D. Long 163, I. Longarini 161, L. Longo 70a,70b, R. Longo 163, I. Lopez Paz 67, A. Lopez Solis 48, J. Lorenz 110, N. Lorenzo Martinez 4, A.M. Lory 110, O. Loseva 37, X. Lou 47a,47b, X. Lou 14a,14e, A. Lounis 66, J. Love 6, P.A. Love 92, G. Lu 14a,14e, M. Lu 80, S. Lu 129, Y.J. Lu 65, H.J. Lubatti 139, C. Luci 75a,75b, F.L. Lucio Alves 14c, A. Lucotte 60, F. Luehring 68, I. Luise 146, O. Lukianchuk 66, O. Lundberg 145, B. Lund-Jensen 145, N.A. Luongo 124, M.S. Lutz 152, D. Lynn 29, H. Lyons93, R. Lysak 132, E. Lytken 99, V. Lyubushkin 38, T. Lyubushkina 38, M.M. Lyukova 146, H. Ma 29, K. Ma62a, L.L. Ma 62b, Y. Ma 122, D.M. Mac Donell 166, G. Maccarrone 53, J.C. MacDonald 101, R. Madar 40, W.F. Mader 50, J. Maeda 85, T. Maeno 29, M. Maerker 50, H. Maguire 140, V. Maiboroda 136, A. Maio 131a,131b,131d, K. Maj 86a, O. Majersky 48, S. Majewski 124, N. Makovec 66, V. Maksimovic 15, B. Malaescu 128, Pa. Malecki 87, V.P. Maleev 37, F. Malek 60, M. Mali 94, D. Malito 96,r, U. Mallik 80, S. Maltezos10, S. Malyukov38, J. Mamuzic 13, G. Mancini 53, G. Manco 73a,73b, J.P. Mandalia 95, I. Mandić 94, L. Manhaes de Andrade Filho 83a, I.M. Maniatis 170, J. Manjarres Ramos 103,ag, D.C. Mankad 170, A. Mann 110, B. Mansoulie 136, S. Manzoni 36, A. Marantis 153,x, G. Marchiori 5, M. Marcisovsky 132, C. Marcon 71a,71b, M. Marinescu 20, M. Marjanovic 121, E.J. Marshall 92, Z. Marshall 17a, S. Marti-Garcia 164, T.A. Martin 168, V.J. Martin 52, B. Martin dit Latour 16, L. Martinelli 75a,75b, M. Martinez 13,y, P. Martinez Agullo 164, V.I. Martinez Outschoorn 104, P. Martinez Suarez 13, S. Martin-Haugh 135, V.S. Martoiu 27b, A.C. Martyniuk 97, A. Marzin 36, D. Mascione 78a,78b, L. Masetti 101, T. Mashimo 154, J. Masik 102, A.L. Maslennikov 37, L. Massa 23b, P. Massarotti 72a,72b, P. Mastrandrea 74a,74b, A. Mastroberardino 43b,43a, T. Masubuchi 154, T. Mathisen 162, J. Matousek 134, N. Matsuzawa154, J. Maurer 27b, B. Maček 94, D.A. Maximov 37, R. Mazini 149, I. Maznas 153, M. Mazza 108, S.M. Mazza 137, E. Mazzeo 71a,71b, C. Mc Ginn 29, J.P. Mc Gowan 105, S.P. Mc Kee 107, E.F. McDonald 106, A.E. McDougall 115, J.A. Mcfayden 147, R.P. McGovern 129, G. Mchedlidze 150b, R.P. Mckenzie 33g, T.C. Mclachlan 48, D.J. Mclaughlin 97, K.D. McLean 166, S.J. McMahon 135, P.C. McNamara 106, C.M. Mcpartland 93, R.A. McPherson 166,ac, S. Mehlhase 110, A. Mehta 93, D. Melini 151, B.R. Mellado Garcia 33g, A.H. Melo 55, F. Meloni 48, A.M. Mendes Jacques Da Costa 102, H.Y. Meng 156, L. Meng 92, S. Menke 111, M. Mentink 36, E. Meoni 43b,43a, C. Merlassino 127, L. Merola 72a,72b, C. Meroni 71a, G. Merz107, O. Meshkov 37, J. Metcalfe 6, A.S. Mete 6, C. Meyer 68, J-P. Meyer 136, R.P. Middleton 135, L. Mijović 52, G. Mikenberg 170, M. Mikestikova 132, M. Mikuž 94, H. Mildner 101, A. Milic 36, C.D. Milke 44, D.W. Miller 39, L.S. Miller 34, A. Milov 170, – 38 – JHEP10(2023)079 D.A. Milstead47a,47b, T. Min14c, A.A. Minaenko 37, I.A. Minashvili 150b, L. Mince 59, A.I. Mincer 118, B. Mindur 86a, M. Mineev 38, Y. Mino 88, L.M. Mir 13, M. Miralles Lopez 164, M. Mironova 17a, A. Mishima154, M.C. Missio 114, T. Mitani 169, A. Mitra 168, V.A. Mitsou 164, O. Miu 156, P.S. Miyagawa 95, Y. Miyazaki90, A. Mizukami 84, T. Mkrtchyan 63a, M. Mlinarevic 97, T. Mlinarevic 97, M. Mlynarikova 36, S. Mobius 19, K. Mochizuki 109, P. Moder 48, P. Mogg 110, A.F. Mohammed 14a,14e, S. Mohapatra 41, G. Mokgatitswane 33g, L. Moleri 170, B. Mondal 142, S. Mondal 133, G. Monig 147, K. Mönig 48, E. Monnier 103, L. Monsonis Romero164, J. Montejo Berlingen 13,84, M. Montella 120, F. Montereali 77a,77b, F. Monticelli 91, S. Monzani 69a,69c, N. Morange 66, A.L. Moreira De Carvalho 131a, M. Moreno Llácer 164, C. Moreno Martinez 56, P. Morettini 57b, S. Morgenstern 36, M. Morii 61, M. Morinaga 154, A.K. Morley 36, F. Morodei 75a,75b, L. Morvaj 36, P. Moschovakos 36, B. Moser 36, M. Mosidze150b, T. Moskalets 54, P. Moskvitina 114, J. Moss 31,p, E.J.W. Moyse 104, O. Mtintsilana 33g, S. Muanza 103, J. Mueller 130, D. Muenstermann 92, R. Müller 19, G.A. Mullier 162, A.J. Mullin32, J.J. Mullin129, D.P. Mungo 156, D. Munoz Perez 164, F.J. Munoz Sanchez 102, M. Murin 102, W.J. Murray 168,135, A. Murrone 71a,71b, J.M. Muse 121, M. Muškinja 17a, C. Mwewa 29, A.G. Myagkov 37,a, A.J. Myers 8, A.A. Myers130, G. Myers 68, M. Myska 133, B.P. Nachman 17a, O. Nackenhorst 49, A. Nag 50, K. Nagai 127, K. Nagano 84, J.L. Nagle 29,an, E. Nagy 103, A.M. Nairz 36, Y. Nakahama 84, K. Nakamura 84, K. Nakkalil 5, H. Nanjo 125, R. Narayan 44, E.A. Narayanan 113, I. Naryshkin 37, M. Naseri 34, S. Nasri 160, C. Nass 24, G. Navarro 22a, J. Navarro-Gonzalez 164, R. Nayak 152, A. Nayaz 18, P.Y. Nechaeva 37, F. Nechansky 48, L. Nedic 127, T.J. Neep 20, A. Negri 73a,73b, M. Negrini 23b, C. Nellist 115, C. Nelson 105, K. Nelson 107, S. Nemecek 132, M. Nessi 36,i, M.S. Neubauer 163, F. Neuhaus 101, J. Neundorf 48, R. Newhouse 165, P.R. Newman 20, C.W. Ng 130, Y.W.Y. Ng 48, B. Ngair 35e, H.D.N. Nguyen 109, R.B. Nickerson 127, R. Nicolaidou 136, J. Nielsen 137, M. Niemeyer 55, J. Niermann 55,36, N. Nikiforou 36, V. Nikolaenko 37,a, I. Nikolic-Audit 128, K. Nikolopoulos 20, P. Nilsson 29, I. Ninca 48, H.R. Nindhito 56, G. Ninio 152, A. Nisati 75a, N. Nishu 2, R. Nisius 111, J-E. Nitschke 50, E.K. Nkadimeng 33g, S.J. Noacco Rosende 91, T. Nobe 154, D.L. Noel 32, T. Nommensen 148, M.B. Norfolk 140, R.R.B. Norisam 97, B.J. Norman 34, J. Novak 94, T. Novak 48, L. Novotny 133, R. Novotny 113, L. Nozka 123, K. Ntekas 161, N.M.J. Nunes De Moura Junior 83b, E. Nurse97, J. Ocariz 128, A. Ochi 85, I. Ochoa 131a, S. Oerdek 162, J.T. Offermann 39, A. Ogrodnik 134, A. Oh 102, C.C. Ohm 145, H. Oide 84, R. Oishi 154, M.L. Ojeda 48, Y. Okazaki 88, M.W. O’Keefe93, Y. Okumura 154, L.F. Oleiro Seabra 131a, S.A. Olivares Pino 138d, D. Oliveira Damazio 29, D. Oliveira Goncalves 83a, J.L. Oliver 161, A. Olszewski 87, Ö.O. Öncel 54, D.C. O’Neil 143, A.P. O’Neill 19, A. Onofre 131a,131e, P.U.E. Onyisi 11, M.J. Oreglia 39, G.E. Orellana 91, D. Orestano 77a,77b, N. Orlando 13, R.S. Orr 156, V. O’Shea 59, L.M. Osojnak 129, R. Ospanov 62a, G. Otero y Garzon 30, H. Otono 90, P.S. Ott 63a, G.J. Ottino 17a, M. Ouchrif 35d, J. Ouellette 29, F. Ould-Saada 126, M. Owen 59, R.E. Owen 135, K.Y. Oyulmaz 21a, V.E. Ozcan 21a, N. Ozturk 8, S. Ozturk 82, H.A. Pacey 32, A. Pacheco Pages 13, C. Padilla Aranda 13, G. Padovano 75a,75b, S. Pagan Griso 17a, – 39 – JHEP10(2023)079 G. Palacino 68, A. Palazzo 70a,70b, S. Palestini 36, J. Pan 173, T. Pan 64a, D.K. Panchal 11, C.E. Pandini 115, J.G. Panduro Vazquez 96, H. Pang 14b, P. Pani 48, G. Panizzo 69a,69c, L. Paolozzi 56, C. Papadatos 109, S. Parajuli 44, A. Paramonov 6, C. Paraskevopoulos 10, D. Paredes Hernandez 64b, T.H. Park 156, M.A. Parker 32, F. Parodi 57b,57a, E.W. Parrish 116, V.A. Parrish 52, J.A. Parsons 41, U. Parzefall 54, B. Pascual Dias 109, L. Pascual Dominguez 152, F. Pasquali 115, E. Pasqualucci 75a, S. Passaggio 57b, F. Pastore 96, P. Pasuwan 47a,47b, P. Patel 87, U.M. Patel 51, J.R. Pater 102, T. Pauly 36, J. Pearkes 144, M. Pedersen 126, R. Pedro 131a, S.V. Peleganchuk 37, O. Penc 36, E.A. Pender 52, H. Peng 62a, K.E. Penski 110, M. Penzin 37, B.S. Peralva 83d, A.P. Pereira Peixoto 60, L. Pereira Sanchez 47a,47b, D.V. Perepelitsa 29,an, E. Perez Codina 157a, M. Perganti 10, L. Perini 71a,71b,∗, H. Pernegger 36, A. Perrevoort 114, O. Perrin 40, K. Peters 48, R.F.Y. Peters 102, B.A. Petersen 36, T.C. Petersen 42, E. Petit 103, V. Petousis 133, C. Petridou 153,f , A. Petrukhin 142, M. Pettee 17a, N.E. Pettersson 36, A. Petukhov 37, K. Petukhova 134, A. Peyaud 136, R. Pezoa 138f, L. Pezzotti 36, G. Pezzullo 173, T.M. Pham 171, T. Pham 106, P.W. Phillips 135, G. Piacquadio 146, E. Pianori 17a, F. Piazza 71a,71b, R. Piegaia 30, D. Pietreanu 27b, A.D. Pilkington 102, M. Pinamonti 69a,69c, J.L. Pinfold 2, B.C. Pinheiro Pereira 131a, A.E. Pinto Pinoargote 136, K.M. Piper 147, A. Pirttikoski 56, C. Pitman Donaldson97, D.A. Pizzi 34, L. Pizzimento 76a,76b, A. Pizzini 115, M.-A. Pleier 29, V. Plesanovs54, V. Pleskot 134, E. Plotnikova38, G. Poddar 4, R. Poettgen 99, L. Poggioli 128, I. Pokharel 55, S. Polacek 134, G. Polesello 73a, A. Poley 143,157a, R. Polifka 133, A. Polini 23b, C.S. Pollard 168, Z.B. Pollock 120, V. Polychronakos 29, E. Pompa Pacchi 75a,75b, D. Ponomarenko 114, L. Pontecorvo 36, S. Popa 27a, G.A. Popeneciu 27d, A. Poreba 36, D.M. Portillo Quintero 157a, S. Pospisil 133, M.A. Postill 140, P. Postolache 27c, K. Potamianos 168, P.A. Potepa 86a, I.N. Potrap 38, C.J. Potter 32, H. Potti 1, T. Poulsen 48, J. Poveda 164, M.E. Pozo Astigarraga 36, A. Prades Ibanez 164, J. Pretel 54, D. Price 102, M. Primavera 70a, M.A. Principe Martin 100, R. Privara 123, T. Procter 59, M.L. Proffitt 139, N. Proklova 129, K. Prokofiev 64c, G. Proto 111, S. Protopopescu 29, J. Proudfoot 6, M. Przybycien 86a, W.W. Przygoda 86b, J.E. Puddefoot 140, D. Pudzha 37, D. Pyatiizbyantseva 37, J. Qian 107, D. Qichen 102, Y. Qin 102, T. Qiu 52, A. Quadt 55, M. Queitsch-Maitland 102, G. Quetant 56, G. Rabanal Bolanos 61, D. Rafanoharana 54, F. Ragusa 71a,71b, J.L. Rainbolt 39, J.A. Raine 56, S. Rajagopalan 29, E. Ramakoti 37, K. Ran 48,14e, N.P. Rapheeha 33g, H. Rasheed 27b, V. Raskina 128, D.F. Rassloff 63a, S. Rave 101, B. Ravina 55, I. Ravinovich 170, M. Raymond 36, A.L. Read 126, N.P. Readioff 140, D.M. Rebuzzi 73a,73b, G. Redlinger 29, A.S. Reed 111, K. Reeves 26, J.A. Reidelsturz 172,w, D. Reikher 152, A. Rej 142, C. Rembser 36, A. Renardi 48, M. Renda 27b, M.B. Rendel111, F. Renner 48, A.G. Rennie 59, S. Resconi 71a, M. Ressegotti 57b,57a, S. Rettie 36, J.G. Reyes Rivera 108, B. Reynolds120, E. Reynolds 17a, O.L. Rezanova 37, P. Reznicek 134, N. Ribaric 92, E. Ricci 78a,78b, R. Richter 111, S. Richter 47a,47b, E. Richter-Was 86b, M. Ridel 128, S. Ridouani 35d, P. Rieck 118, P. Riedler 36, M. Rijssenbeek 146, A. Rimoldi 73a,73b, M. Rimoldi 48, L. Rinaldi 23b,23a, T.T. Rinn 29, M.P. Rinnagel 110, G. Ripellino 162, I. Riu 13, P. Rivadeneira 48, J.C. Rivera Vergara 166, F. Rizatdinova 122, – 40 – JHEP10(2023)079 77 (a)INFN Sezione di Roma Tre;(b)Dipartimento di Matematica e Fisica, Università Roma Tre, Roma; Italy 78 (a)INFN-TIFPA;(b)Università degli Studi di Trento, Trento; Italy 79 Universität Innsbruck, Department of Astro and Particle Physics, Innsbruck; Austria 80 University of Iowa, Iowa City IA; United States of America 81 Department of Physics and Astronomy, Iowa State University, Ames IA; United States of America 82 Istinye University, Sariyer, Istanbul; Türkiye 83 (a)Departamento de Engenharia Elétrica, Universidade Federal de Juiz de Fora (UFJF), Juiz de Fora;(b)Universidade Federal do Rio De Janeiro COPPE/EE/IF, Rio de Janeiro;(c)Instituto de Física, Universidade de São Paulo, São Paulo;(d)Rio de Janeiro State University, Rio de Janeiro; Brazil 84 KEK, High Energy Accelerator Research Organization, Tsukuba; Japan 85 Graduate School of Science, Kobe University, Kobe; Japan 86 (a)AGH University of Krakow, Faculty of Physics and Applied Computer Science, Krakow;(b)Marian Smoluchowski Institute of Physics, Jagiellonian University, Krakow; Poland 87 Institute of Nuclear Physics Polish Academy of Sciences, Krakow; Poland 88 Faculty of Science, Kyoto University, Kyoto; Japan 89 Kyoto University of Education, Kyoto; Japan 90 Research Center for Advanced Particle Physics and Department of Physics, Kyushu University, Fukuoka; Japan 91 Instituto de Física La Plata, Universidad Nacional de La Plata and CONICET, La Plata; Argentina 92 Physics Department, Lancaster University, Lancaster; United Kingdom 93 Oliver Lodge Laboratory, University of Liverpool, Liverpool; United Kingdom 94 Department of Experimental Particle Physics, Jožef Stefan Institute and Department of Physics, University of Ljubljana, Ljubljana; Slovenia 95 School of Physics and Astronomy, Queen Mary University of London, London; United Kingdom 96 Department of Physics, Royal Holloway University of London, Egham; United Kingdom 97 Department of Physics and Astronomy, University College London, London; United Kingdom 98 Louisiana Tech University, Ruston LA; United States of America 99 Fysiska institutionen, Lunds universitet, Lund; Sweden 100 Departamento de Física Teorica C-15 and CIAFF, Universidad Autónoma de Madrid, Madrid; Spain 101 Institut für Physik, Universität Mainz, Mainz; Germany 102 School of Physics and Astronomy, University of Manchester, Manchester; United Kingdom 103 CPPM, Aix-Marseille Université, CNRS/IN2P3, Marseille; France 104 Department of Physics, University of Massachusetts, Amherst MA; United States of America 105 Department of Physics, McGill University, Montreal QC; Canada 106 School of Physics, University of Melbourne, Victoria; Australia 107 Department of Physics, University of Michigan, Ann Arbor MI; United States of America 108 Department of Physics and Astronomy, Michigan State University, East Lansing MI; United States of America 109 Group of Particle Physics, University of Montreal, Montreal QC; Canada 110 Fakultät für Physik, Ludwig-Maximilians-Universität München, München; Germany 111 Max-Planck-Institut für Physik (Werner-Heisenberg-Institut), München; Germany 112 Graduate School of Science and Kobayashi-Maskawa Institute, Nagoya University, Nagoya; Japan 113 Department of Physics and Astronomy, University of New Mexico, Albuquerque NM; United States of America 114 Institute for Mathematics, Astrophysics and Particle Physics, Radboud University/Nikhef, Nijmegen; Netherlands 115 Nikhef National Institute for Subatomic Physics and University of Amsterdam, Amsterdam; Netherlands 116 Department of Physics, Northern Illinois University, DeKalb IL; United States of America 117 (a)New York University Abu Dhabi, Abu Dhabi;(b)University of Sharjah, Sharjah; United Arab Emirates – 47 – JHEP10(2023)079 118 Department of Physics, New York University, New York NY; United States of America 119 Ochanomizu University, Otsuka, Bunkyo-ku, Tokyo; Japan 120 Ohio State University, Columbus OH; United States of America 121 Homer L. Dodge Department of Physics and Astronomy, University of Oklahoma, Norman OK; United States of America 122 Department of Physics, Oklahoma State University, Stillwater OK; United States of America 123 Palacký University, Joint Laboratory of Optics, Olomouc; Czech Republic 124 Institute for Fundamental Science, University of Oregon, Eugene, OR; United States of America 125 Graduate School of Science, Osaka University, Osaka; Japan 126 Department of Physics, University of Oslo, Oslo; Norway 127 Department of Physics, Oxford University, Oxford; United Kingdom 128 LPNHE, Sorbonne Université, Université Paris Cité, CNRS/IN2P3, Paris; France 129 Department of Physics, University of Pennsylvania, Philadelphia PA; United States of America 130 Department of Physics and Astronomy, University of Pittsburgh, Pittsburgh PA; United States of America 131 (a)Laboratório de Instrumentação e Física Experimental de Partículas — LIP, Lisboa;(b)Departamento de Física, Faculdade de Ciências, Universidade de Lisboa, Lisboa;(c)Departamento de Física, Universidade de Coimbra, Coimbra;(d)Centro de Física Nuclear da Universidade de Lisboa, Lisboa;(e)Departamento de Física, Universidade do Minho, Braga;(f)Departamento de Física Teórica y del Cosmos, Universidad de Granada, Granada (Spain);(g)Departamento de Física, Instituto Superior Técnico, Universidade de Lisboa, Lisboa; Portugal 132 Institute of Physics of the Czech Academy of Sciences, Prague; Czech Republic 133 Czech Technical University in Prague, Prague; Czech Republic 134 Charles University, Faculty of Mathematics and Physics, Prague; Czech Republic 135 Particle Physics Department, Rutherford Appleton Laboratory, Didcot; United Kingdom 136 IRFU, CEA, Université Paris-Saclay, Gif-sur-Yvette; France 137 Santa Cruz Institute for Particle Physics, University of California Santa Cruz, Santa Cruz CA; United States of America 138 (a)Departamento de Física, Pontificia Universidad Católica de Chile, Santiago;(b)Millennium Institute for Subatomic physics at high energy frontier (SAPHIR), Santiago;(c)Instituto de Investigación Multidisciplinario en Ciencia y Tecnología, y Departamento de Física, Universidad de La Serena;(d)Universidad Andres Bello, Department of Physics, Santiago;(e)Instituto de Alta Investigación, Universidad de Tarapacá, Arica;(f)Departamento de Física, Universidad Técnica Federico Santa María, Valparaíso; Chile 139 Department of Physics, University of Washington, Seattle WA; United States of America 140 Department of Physics and Astronomy, University of Sheffield, Sheffield; United Kingdom 141 Department of Physics, Shinshu University, Nagano; Japan 142 Department Physik, Universität Siegen, Siegen; Germany 143 Department of Physics, Simon Fraser University, Burnaby BC; Canada 144 SLAC National Accelerator Laboratory, Stanford CA; United States of America 145 Department of Physics, Royal Institute of Technology, Stockholm; Sweden 146 Departments of Physics and Astronomy, Stony Brook University, Stony Brook NY; United States of America 147 Department of Physics and Astronomy, University of Sussex, Brighton; United Kingdom 148 School of Physics, University of Sydney, Sydney; Australia 149 Institute of Physics, Academia Sinica, Taipei; Taiwan 150 (a)E. Andronikashvili Institute of Physics, Iv. Javakhishvili Tbilisi State University, Tbilisi;(b)High Energy Physics Institute, Tbilisi State University, Tbilisi;(c)University of Georgia, Tbilisi; Georgia 151 Department of Physics, Technion, Israel Institute of Technology, Haifa; Israel 152 Raymond and Beverly Sackler School of Physics and Astronomy, Tel Aviv University, Tel Aviv; Israel 153 Department of Physics, Aristotle University of Thessaloniki, Thessaloniki; Greece 154 International Center for Elementary Particle Physics and Department of Physics, University of Tokyo, Tokyo; Japan – 48 – JHEP10(2023)079 155 Department of Physics, Tokyo Institute of Technology, Tokyo; Japan 156 Department of Physics, University of Toronto, Toronto ON; Canada 157 (a)TRIUMF, Vancouver BC;(b)Department of Physics and Astronomy, York University, Toronto ON; Canada 158 Division of Physics and Tomonaga Center for the History of the Universe, Faculty of Pure and Applied Sciences, University of Tsukuba, Tsukuba; Japan 159 Department of Physics and Astronomy, Tufts University, Medford MA; United States of America 160 United Arab Emirates University, Al Ain; United Arab Emirates 161 Department of Physics and Astronomy, University of California Irvine, Irvine CA; United States of America 162 Department of Physics and Astronomy, University of Uppsala, Uppsala; Sweden 163 Department of Physics, University of Illinois, Urbana IL; United States of America 164 Instituto de Física Corpuscular (IFIC), Centro Mixto Universidad de Valencia — CSIC, Valencia; Spain 165 Department of Physics, University of British Columbia, Vancouver BC; Canada 166 Department of Physics and Astronomy, University of Victoria, Victoria BC; Canada 167 Fakultät für Physik und Astronomie, Julius-Maximilians-Universität Würzburg, Würzburg; Germany 168 Department of Physics, University of Warwick, Coventry; United Kingdom 169 Waseda University, Tokyo; Japan 170 Department of Particle Physics and Astrophysics, Weizmann Institute of Science, Rehovot; Israel 171 Department of Physics, University of Wisconsin, Madison WI; United States of America 172 Fakultät für Mathematik und Naturwissenschaften, Fachgruppe Physik, Bergische Universität Wuppertal, Wuppertal; Germany 173 Department of Physics, Yale University, New Haven CT; United States of America aAlso Affiliated with an institute covered by a cooperation agreement with CERN bAlso at An-Najah National University, Nablus; Palestine cAlso at APC, Université Paris Cité, CNRS/IN2P3, Paris; France dAlso at Borough of Manhattan Community College, City University of New York, New York NY; United States of America eAlso at Center for High Energy Physics, Peking University; China fAlso at Center for Interdisciplinary Research and Innovation (CIRI-AUTH), Thessaloniki; Greece gAlso at Centro Studi e Ricerche Enrico Fermi; Italy hAlso at CERN, Geneva; Switzerland iAlso at Département de Physique Nucléaire et Corpusculaire, Université de Genève, Genève; Switzerland jAlso at Departament de Fisica de la Universitat Autonoma de Barcelona, Barcelona; Spain kAlso at Department of Financial and Management Engineering, University of the Aegean, Chios; Greece lAlso at Department of Physics and Astronomy, Michigan State University, East Lansing MI; United States of America mAlso at Department of Physics and Astronomy, University of Victoria, Victoria BC; Canada nAssociated at Department of Physics, Ben Gurion University of the Negev, Beer Sheva; Israel oAlso at Department of Physics, Ben Gurion University of the Negev, Beer Sheva; Israel pAlso at Department of Physics, California State University, Sacramento; United States of America qAlso at Department of Physics, King’s College London, London; United Kingdom rAlso at Department of Physics, Royal Holloway University of London, Egham; United Kingdom sAlso at Department of Physics, Stanford University, Stanford CA; United States of America tAlso at Department of Physics, University of Fribourg, Fribourg; Switzerland uAlso at Department of Physics, University of Thessaly; Greece vAlso at Department of Physics, Westmont College, Santa Barbara; United States of America wAlso at Fakultät für Mathematik und Naturwissenschaften, Fachgruppe Physik, Bergische Universität Wuppertal, Wuppertal; Germany – 49 – JHEP10(2023)079 xAlso at Hellenic Open University, Patras; Greece yAlso at Institucio Catalana de Recerca i Estudis Avancats, ICREA, Barcelona; Spain zAlso at Institut für Experimentalphysik, Universität Hamburg, Hamburg; Germany aa Also at Institute for Nuclear Research and Nuclear Energy (INRNE) of the Bulgarian Academy of Sciences, Sofia; Bulgaria ab Also at Institute of Applied Physics, Mohammed VI Polytechnic University, Ben Guerir; Morocco ac Also at Institute of Particle Physics (IPP); Canada ad Also at Institute of Physics and Technology, Ulaanbaatar; Mongolia ae Also at Institute of Physics, Azerbaijan Academy of Sciences, Baku; Azerbaijan af Also at Institute of Theoretical Physics, Ilia State University, Tbilisi; Georgia ag Also at L2IT, Université de Toulouse, CNRS/IN2P3, UPS, Toulouse; France ah Also at Lawrence Livermore National Laboratory, Livermore; United States of America ai Also at National Institute of Physics, University of the Philippines Diliman (Philippines); Philippines aj Also at Technical University of Munich, Munich; Germany ak Also at The Collaborative Innovation Center of Quantum Matter (CICQM), Beijing; China al Also at TRIUMF, Vancouver BC; Canada am Also at Università di Napoli Parthenope, Napoli; Italy an Also at University of Colorado Boulder, Department of Physics, Colorado; United States of America ao Also at Washington College, Chestertown, MD; United States of America ap Also at Yeditepe University, Physics Department, Istanbul; Türkiye ∗Deceased – 50 –